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{ WritableStream } from 'node:stream/web';\nimport type { DataChunkType } from '../stream/types';\nimport type { OutputWriter } from '../workflows';\n\nexport class ToolStream extends WritableStream<unknown> {\n  private prefix: string;\n  private callId: string;\n  private name: string;\n  private runId: string;\n  private writeFn?: OutputWriter;\n\n  constructor(\n    {\n      prefix,\n      callId,\n      name,\n      runId,\n    }: {\n      prefix: string;\n      callId: string;\n      name: string;\n      runId: string;\n    },\n    writeFn?: OutputWriter,\n  ) {\n    super({\n      async write(chunk: any) {\n        await getInstance()._write(chunk);\n      },\n    });\n\n    const self = this;\n    function getInstance() {\n      return self;\n    }\n\n    this.prefix = prefix;\n    this.callId = callId;\n    this.name = name;\n    this.runId = runId;\n    this.writeFn = writeFn;\n  }\n\n  private async _write(data: any) {\n    if (this.writeFn) {\n      await this.writeFn({\n        type: `${this.prefix}-output`,\n        runId: this.runId,\n        from: 'USER',\n        payload: {\n          output: data,\n          ...(this.prefix === 'workflow-step'\n            ? {\n                runId: this.runId,\n                stepName: this.name,\n              }\n            : {\n                [`${this.prefix}CallId`]: this.callId,\n                [`${this.prefix}Name`]: this.name,\n              }),\n        },\n      });\n    }\n  }\n\n  async write(data: any) {\n    await this._write(data);\n  }\n\n  async custom<T extends { type: string }>(data: T extends { type: `data-${string}` } ? DataChunkType : T) {\n    if (this.writeFn) {\n      await this.writeFn(data);\n    }\n  }\n}\n","import type { WritableStream } from 'node:stream/web';\nimport type {\n  Tool,\n  ToolV5,\n  FlexibleSchema,\n  ToolCallOptions,\n  ToolExecutionOptions,\n  Schema,\n} from '@internal/external-types';\nimport type { RequestHandlerExtra } from '@modelcontextprotocol/sdk/shared/protocol.js';\nimport type { ElicitRequest, ElicitResult } from '@modelcontextprotocol/sdk/types.js';\n\nimport type { MastraPrimitives, MastraUnion } from '../action';\nexport type { MastraPrimitives, MastraUnion };\nimport type { ActorSignal } from '../auth/ee';\nimport type { ToolBackgroundConfig } from '../background-tasks';\nimport type { MastraBrowser } from '../browser/browser';\nimport type { Mastra } from '../mastra';\nimport type { ObservabilityContext } from '../observability';\nimport type { RequestContext } from '../request-context';\nimport type { PublicSchema } from '../schema';\nimport type { SuspendOptions, OutputWriter } from '../workflows';\nimport type { Workspace } from '../workspace/workspace';\nimport type { ToolStream } from './stream';\nimport type { ValidationError } from './validation';\n\nexport type VercelTool = Tool;\nexport type VercelToolV5 = ToolV5;\n\nexport type ToolInvocationOptions = ToolExecutionOptions | ToolCallOptions;\n\n/**\n * Context passed to a global `requireToolApproval` function, evaluated per tool call.\n */\nexport type ToolApprovalContext = {\n  /** Name of the tool being called. */\n  toolName: string;\n  /** Arguments the model is passing to the tool. */\n  args: Record<string, unknown>;\n  /** Plain object view of the request context, when available. */\n  requestContext?: Record<string, unknown>;\n  /** Active workspace, when the run is bound to one. */\n  workspace?: Workspace;\n};\n\n/**\n * Function form of the global `requireToolApproval` option. Evaluated per tool call;\n * return `true` to require approval for that call, `false` to allow it. Enables\n * conditional, per-call approval policies (e.g. regex matching on `toolName`).\n */\nexport type RequireToolApprovalFn = (ctx: ToolApprovalContext) => boolean | Promise<boolean>;\n\n/**\n * Global tool approval setting. `true` requires approval for every tool call,\n * `false`/omitted requires none, and a function decides per call.\n */\nexport type RequireToolApproval = boolean | RequireToolApprovalFn;\n\n/**\n * Context passed to a per-tool `needsApprovalFn` alongside the parsed tool input.\n * This is the same context surfaced to a tool-level `requireApproval` function.\n */\nexport type NeedsApprovalContext = {\n  /** Plain object view of the request context, when available. */\n  requestContext?: Record<string, unknown>;\n  /** Active workspace, when the run is bound to one. */\n  workspace?: Workspace;\n};\n\n/**\n * Per-tool approval predicate attached to a tool instance.\n *\n * This is the runtime-resolved form of a tool's `requireApproval` function (or of an\n * MCP server-level `requireToolApproval` function wrapped by the MCP client). It is\n * evaluated per tool call with the parsed input and the available context; return\n * `true` to require approval for that call, `false` to allow it.\n *\n * It is attached to the tool instance by {@link CoreToolBuilder} / the MCP client and\n * read by the agent runtime. Prefer the public `requireApproval` option on\n * `createTool` over setting this directly.\n */\nexport type NeedsApprovalFn = (input: any, ctx?: NeedsApprovalContext) => boolean | Promise<boolean>;\n\nexport interface ToolHookContext<\n  TInput = unknown,\n  TContext = unknown,\n  TMetadata extends Record<string, unknown> = Record<string, unknown>,\n> {\n  /** The name exposed to the model for this tool call. */\n  toolName: string;\n  /** Input passed to the tool. */\n  input: TInput;\n  /** Execution context passed to the tool. */\n  context: TContext;\n  /** Optional adapter-specific metadata. */\n  metadata?: TMetadata;\n}\n\nexport interface ToolBeforeHookResult<TOutput = unknown> {\n  /** Set to false to skip the tool execution and return `output` instead. */\n  proceed: false;\n  output: TOutput;\n}\n\nexport interface ToolAfterHookContext<\n  TInput = unknown,\n  TOutput = unknown,\n  TContext = unknown,\n  TMetadata extends Record<string, unknown> = Record<string, unknown>,\n> extends ToolHookContext<TInput, TContext, TMetadata> {\n  /** Tool output when execution completed. Undefined when execution failed before producing output. */\n  output?: TOutput;\n  /** Error thrown by the tool, if execution failed. */\n  error?: unknown;\n}\n\nexport interface ToolHooks<\n  TInput = unknown,\n  TOutput = unknown,\n  TContext = unknown,\n  TMetadata extends Record<string, unknown> = Record<string, unknown>,\n> {\n  beforeToolCall?: (\n    context: ToolHookContext<TInput, TContext, TMetadata>,\n  ) => void | ToolBeforeHookResult<TOutput> | Promise<void | ToolBeforeHookResult<TOutput>>;\n  afterToolCall?: (context: ToolAfterHookContext<TInput, TOutput, TContext, TMetadata>) => void | Promise<void>;\n}\n\nexport type ToolPayloadTransformTarget = 'display' | 'transcript';\n\nexport type ToolPayloadTransformPhase =\n  | 'input-delta'\n  | 'input-available'\n  | 'output-available'\n  | 'error'\n  | 'approval'\n  | 'suspend'\n  | 'resume';\n\nexport type ToolPayloadTransformContext<TInput = unknown, TOutput = unknown, TError = unknown> = {\n  target: ToolPayloadTransformTarget;\n  phase: ToolPayloadTransformPhase;\n  toolName: string;\n  toolCallId: string;\n  input?: TInput;\n  inputTextDelta?: string;\n  output?: TOutput;\n  error?: TError;\n  suspendPayload?: unknown;\n  resumeData?: unknown;\n  providerMetadata?: Record<string, unknown>;\n  context?: Record<string, unknown>;\n};\n\nexport type ToolPayloadTransformResult = unknown;\n\nexport type ToolPayloadTransformFunction<TInput = unknown, TOutput = unknown, TError = unknown> = (\n  context: ToolPayloadTransformContext<TInput, TOutput, TError>,\n) => ToolPayloadTransformResult | Promise<ToolPayloadTransformResult>;\n\nexport type ToolPayloadTransformTargetConfig<TInput = unknown, TOutput = unknown, TError = unknown> = {\n  input?: ToolPayloadTransformFunction<TInput, TOutput, TError>;\n  inputDelta?: ToolPayloadTransformFunction<TInput, TOutput, TError>;\n  output?: ToolPayloadTransformFunction<TInput, TOutput, TError>;\n  error?: ToolPayloadTransformFunction<TInput, TOutput, TError>;\n  approval?: ToolPayloadTransformFunction<TInput, TOutput, TError>;\n  suspend?: ToolPayloadTransformFunction<TInput, TOutput, TError>;\n  resume?: ToolPayloadTransformFunction<TInput, TOutput, TError>;\n};\n\nexport type ToolPayloadTransform<TInput = unknown, TOutput = unknown, TError = unknown> = Partial<\n  Record<ToolPayloadTransformTarget, ToolPayloadTransformTargetConfig<TInput, TOutput, TError>>\n>;\n\nexport type ToolPayloadTransformPolicy = {\n  transformToolPayload?: ToolPayloadTransformFunction;\n  targets?: ToolPayloadTransformTarget[];\n};\n\n/**\n * Observability helpers available on the tool execution context.\n * Wraps child span creation and structured log emission in a\n * null-safe API that callers never need to check — when no tracing\n * context is active, `span` runs the function directly and `log` is\n * a no-op.\n */\nexport interface ToolObserve {\n  span<T>(name: string, fn: () => Promise<T> | T, attributes?: Record<string, unknown>): Promise<T>;\n  log(level: 'debug' | 'info' | 'warn' | 'error' | 'fatal', message: string, data?: Record<string, unknown>): void;\n}\n\n/**\n * A no-op ToolObserve implementation. `span` runs the function\n * directly; `log` does nothing. Used as the default when no\n * collector/tracing context is active, so user code never needs to\n * null-check `observe`.\n */\nexport const noopObserve: ToolObserve = {\n  async span<T>(_name: string, fn: () => Promise<T> | T): Promise<T> {\n    return fn();\n  },\n  log(): void {},\n};\n\n/**\n * MCP-specific context properties available during tool execution in MCP environments.\n */\n// Agent tool execution context - properties specific when tools are executed by agents\nexport interface AgentToolExecutionContext<TSuspend, TResume> {\n  // Always present when called from agent context\n  agentId: string;\n  toolCallId: string;\n  messages: any[];\n  suspend: (suspendPayload: TSuspend, suspendOptions?: SuspendOptions) => Promise<void>;\n\n  // Optional - memory identifiers\n  threadId?: string;\n  resourceId?: string;\n\n  // Optional - only present if tool was previously suspended\n  resumeData?: TResume;\n\n  // Optional - original WritableStream passed from AI SDK (without Mastra metadata wrapping)\n  writableStream?: WritableStream<any>;\n\n  /**\n   * Flushes the parent stream's pending messages to persistent storage.\n   * See `MastraToolInvocationOptions.flushMessages` for details.\n   */\n  flushMessages?: () => Promise<void>;\n}\n\n// Workflow tool execution context - properties specific when tools are executed in workflows\nexport interface WorkflowToolExecutionContext<TSuspend, TResume> {\n  // Always present when called from workflow context\n  runId: string;\n  workflowId: string;\n  state: any;\n  setState: (state: any) => void;\n  suspend: (suspendPayload: TSuspend, suspendOptions?: SuspendOptions) => Promise<void>;\n  // Optional - only present if workflow step was previously suspended\n  resumeData?: TResume;\n}\n\n// MCP tool execution context - properties specific when tools are executed via Model Context Protocol\nexport interface MCPToolExecutionContext {\n  /** MCP protocol context passed by the server */\n  extra: RequestHandlerExtra<any, any>;\n  /** Elicitation handler for interactive user input during tool execution */\n  elicitation: {\n    sendRequest: (request: ElicitRequest['params']) => Promise<ElicitResult>;\n  };\n}\n\n/**\n * Extended version of ToolInvocationOptions that includes Mastra-specific properties\n * for suspend/resume functionality, stream writing, and tracing context.\n *\n * This is used by CoreTool/InternalCoreTool for AI SDK compatibility (AI SDK expects this signature).\n * Mastra v1.0 tools (ToolAction) use ToolExecutionContext instead.\n *\n * CoreToolBuilder acts as the adapter layer:\n * - Receives: AI SDK calls with MastraToolInvocationOptions\n * - Converts to: ToolExecutionContext for Mastra tool execution\n * - Returns: Results back to AI SDK\n */\nexport type MastraToolInvocationOptions = ToolInvocationOptions &\n  Partial<ObservabilityContext> & {\n    suspend?: (suspendPayload: any, suspendOptions?: SuspendOptions) => Promise<any>;\n    resumeData?: any;\n    outputWriter?: OutputWriter;\n    /**\n     * Optional MCP-specific context passed when tool is executed in MCP server.\n     * This is populated by the MCP server and passed through to the tool's execution context.\n     */\n    mcp?: MCPToolExecutionContext;\n    /**\n     * Workspace for tool execution. When provided at execution time, this overrides\n     * any workspace configured at tool build time. Allows dynamic workspace selection\n     * per-step via prepareStep.\n     */\n    workspace?: Workspace;\n    /**\n     * Request context for tool execution. When provided at execution time, this overrides\n     * any requestContext configured at tool build time. Allows workflow steps to forward\n     * their requestContext (e.g., authenticated API clients, feature flags) to tools.\n     */\n    requestContext?: RequestContext;\n    /** Trusted server-side signal for this tool FGA check. */\n    actor?: ActorSignal;\n    /**\n     * Flushes the parent stream's pending messages to persistent storage.\n     *\n     * The agent stream batches message saves through a `SaveQueueManager`\n     * (100ms debounce). Tools that read the thread's persisted history\n     * mid-stream (e.g. cloning the thread, exporting it, handing off to a\n     * sibling agent) must call this first, otherwise the store will be\n     * missing the latest user / assistant messages.\n     *\n     * Populated automatically by the agent tool-call step. No-op when the\n     * stream is not memory-backed.\n     */\n    flushMessages?: () => Promise<void>;\n    /** Observability helper to expose on the final tool execution context. */\n    observe?: ToolObserve;\n  };\n\n/**\n * The type of tool registered with the MCP server.\n * This is used to categorize tools in the MCP Server playground.\n * If not specified, it defaults to a regular tool.\n */\nexport type MCPToolType = 'agent' | 'workflow';\n\n/**\n * Metadata identifying a tool as originating from an MCP server.\n * Set automatically by the MCP client when creating tools.\n * Used by CoreToolBuilder to create MCP_TOOL_CALL spans instead of TOOL_CALL spans.\n */\nexport interface McpMetadata {\n  serverName: string;\n  serverVersion?: string;\n  /** Instructions advertised by the MCP server during initialize. */\n  serverInstructions?: string;\n  /** Whether the agent should append these instructions to its system prompt. Defaults to false (opt-in). */\n  forwardInstructions?: boolean;\n  /** Maximum number of characters to forward into the agent system prompt. */\n  instructionsMaxLength?: number;\n}\n\n/**\n * MCP Tool Annotations for describing tool behavior and UI presentation.\n * These annotations are part of the MCP protocol and are used by clients\n * like OpenAI Apps SDK to control tool card display and permission hints.\n *\n * @see https://spec.modelcontextprotocol.io/specification/2025-03-26/server/tools/#tool-annotations\n */\nexport interface ToolAnnotations {\n  /**\n   * A human-readable title for the tool.\n   * Used for display purposes in UI components.\n   */\n  title?: string;\n  /**\n   * If true, the tool does not modify its environment.\n   * This hint indicates the tool only reads data and has no side effects.\n   * @default false\n   */\n  readOnlyHint?: boolean;\n  /**\n   * If true, the tool may perform destructive updates to its environment.\n   * If false, the tool performs only additive updates.\n   * This hint helps clients determine if confirmation should be required.\n   * @default true\n   */\n  destructiveHint?: boolean;\n  /**\n   * If true, calling the tool repeatedly with the same arguments\n   * will have no additional effect on its environment.\n   * This hint indicates idempotent behavior.\n   * @default false\n   */\n  idempotentHint?: boolean;\n  /**\n   * If true, this tool may interact with an \"open world\" of external\n   * entities (e.g., web search, external APIs).\n   * If false, the tool's domain is closed and fully defined.\n   * @default true\n   */\n  openWorldHint?: boolean;\n}\n\n// MCP-specific properties for tools\nexport interface MCPToolProperties {\n  /**\n   * The type of tool registered with the MCP server.\n   * This is used to categorize tools in the MCP Server playground.\n   * If not specified, it defaults to a regular tool.\n   */\n  toolType?: MCPToolType;\n  /**\n   * MCP tool annotations for describing tool behavior and UI presentation.\n   * These are exposed via MCP protocol and used by clients like OpenAI Apps SDK.\n   * @see https://spec.modelcontextprotocol.io/specification/2025-03-26/server/tools/#tool-annotations\n   */\n  annotations?: ToolAnnotations;\n  /**\n   * Arbitrary metadata that will be passed through to MCP clients.\n   * This field allows custom metadata to be attached to tools for\n   * client-specific functionality.\n   */\n  _meta?: Record<string, unknown>;\n}\n\n/**\n * CoreTool is the AI SDK-compatible tool format used when passing tools to the AI SDK.\n * This matches the AI SDK's Tool interface.\n *\n * CoreToolBuilder converts Mastra tools (ToolAction) to this format and handles the\n * signature transformation from Mastra's (inputData, context) to AI SDK format (params, options).\n *\n * Key differences from ToolAction:\n * - Uses 'parameters' instead of 'inputSchema' (AI SDK naming)\n * - Execute signature: (params, options: MastraToolInvocationOptions) (AI SDK format)\n * - Supports FlexibleSchema | Schema for broader AI SDK compatibility\n */\nexport type CoreTool = {\n  description?: string;\n  parameters: FlexibleSchema<any> | Schema;\n  outputSchema?: FlexibleSchema<any> | Schema;\n  execute?: (params: any, options: MastraToolInvocationOptions) => Promise<any>;\n  /**\n   * Enables strict tool input generation for providers that support it.\n   */\n  strict?: boolean;\n  /**\n   * Provider-specific options passed to the model when this tool is used.\n   */\n  providerOptions?: Record<string, Record<string, unknown>>;\n  /**\n   * Optional MCP-specific properties.\n   * Only populated when the tool is being used in an MCP context.\n   */\n  mcp?: MCPToolProperties;\n  /**\n   * Optional function to transform tool output before returning to the model.\n   * Receives the raw tool output and returns a transformed representation.\n   * Passed through from the original tool definition.\n   */\n  toModelOutput?: (output: unknown) => unknown;\n  transform?: ToolPayloadTransform;\n  /**\n   * Examples of valid tool inputs. Each example contains an `input` object\n   * showing what valid arguments look like.\n   * Passed through to the AI SDK which forwards them to model providers\n   * that support input examples (e.g., Anthropic's `input_examples` beta feature).\n   */\n  inputExamples?: Array<{ input: Record<string, unknown> }>;\n  onInputStart?: (options: ToolCallOptions) => void | PromiseLike<void>;\n  onInputDelta?: (options: { inputTextDelta: string } & ToolCallOptions) => void | PromiseLike<void>;\n  onInputAvailable?: (options: { input: any } & ToolCallOptions) => void | PromiseLike<void>;\n  onOutput?: (\n    options: { output: any; toolName: string } & Omit<ToolCallOptions, 'messages'>,\n  ) => void | PromiseLike<void>;\n  /** Background task configuration for this tool. */\n  background?: ToolBackgroundConfig;\n} & (\n  | {\n      type?: 'function' | undefined;\n      id?: string;\n    }\n  | {\n      type: 'provider-defined';\n      id: `${string}.${string}`;\n      args: Record<string, unknown>;\n    }\n);\n\n/**\n * InternalCoreTool is identical to CoreTool but with stricter typing.\n * Used internally where we know the schema has already been converted to AI SDK Schema format.\n *\n * The only difference: parameters must be Schema (not FlexibleSchema | Schema)\n */\nexport type InternalCoreTool = {\n  description?: string;\n  parameters: Schema;\n  outputSchema?: Schema;\n  execute?: (params: any, options: MastraToolInvocationOptions) => Promise<any>;\n  /**\n   * Enables strict tool input generation for providers that support it.\n   */\n  strict?: boolean;\n  /**\n   * Provider-specific options passed to the model when this tool is used.\n   */\n  providerOptions?: Record<string, Record<string, unknown>>;\n  /**\n   * Optional MCP-specific properties.\n   * Only populated when the tool is being used in an MCP context.\n   */\n  mcp?: MCPToolProperties;\n  /**\n   * Optional function to transform tool output before returning to the model.\n   * Receives the raw tool output and returns a transformed representation.\n   * Passed through from the original tool definition.\n   */\n  toModelOutput?: (output: unknown) => unknown;\n  transform?: ToolPayloadTransform;\n  /**\n   * Examples of valid tool inputs. Each example contains an `input` object\n   * showing what valid arguments look like.\n   * Passed through to the AI SDK which forwards them to model providers\n   * that support input examples (e.g., Anthropic's `input_examples` beta feature).\n   */\n  inputExamples?: Array<{ input: Record<string, unknown> }>;\n  onInputStart?: (options: ToolCallOptions) => void | PromiseLike<void>;\n  onInputDelta?: (options: { inputTextDelta: string } & ToolCallOptions) => void | PromiseLike<void>;\n  onInputAvailable?: (options: { input: any } & ToolCallOptions) => void | PromiseLike<void>;\n  onOutput?: (\n    options: { output: any; toolName: string } & Omit<ToolCallOptions, 'messages'>,\n  ) => void | PromiseLike<void>;\n  /** Background task configuration for this tool. */\n  background?: ToolBackgroundConfig;\n} & (\n  | {\n      type?: 'function' | undefined;\n      id?: string;\n    }\n  | {\n      type: 'provider-defined';\n      id: `${string}.${string}`;\n      args: Record<string, unknown>;\n    }\n);\n\n// Unified tool execution context that works for all scenarios\nexport interface ToolExecutionContext<\n  TSuspend = unknown,\n  TResume = unknown,\n  TRequestContext extends Record<string, any> | unknown = unknown,\n> extends Partial<ObservabilityContext> {\n  // ============ Common properties (available in all contexts) ============\n  mastra?: MastraUnion;\n  requestContext?: RequestContext<TRequestContext>;\n  abortSignal?: AbortSignal;\n  /** Trusted server-side signal forwarded for nested FGA checks. */\n  actor?: ActorSignal;\n\n  /**\n   * Workspace available for tool execution. When provided, tools can access:\n   * - workspace.filesystem - for file operations (read, write, list, etc.)\n   * - workspace.sandbox - for command execution\n   *\n   * This allows tools to work with the agent's configured workspace.\n   */\n  workspace?: Workspace;\n\n  /**\n   * Browser available for tool execution. When provided, tools can access\n   * browser capabilities for web automation, screenshots, and data extraction.\n   *\n   * The browser is lazily initialized - it will be launched on first use.\n   */\n  browser?: MastraBrowser;\n\n  // Writer is created by Mastra for ALL contexts (agent, workflow, direct execution)\n  // Wraps chunks with metadata (toolCallId, toolName, runId) before passing to underlying stream\n  writer?: ToolStream;\n\n  // ============ Context-specific nested properties ============\n\n  // Agent-specific properties\n  agent?: AgentToolExecutionContext<TSuspend, TResume>;\n\n  // Workflow-specific properties\n  workflow?: WorkflowToolExecutionContext<TSuspend, TResume>;\n\n  // MCP (Model Context Protocol) specific context\n  mcp?: MCPToolExecutionContext;\n\n  /**\n   * Observability helpers for recording child spans and structured logs\n   * from inside a tool's execute function. Always provided — when no\n   * tracing context is active, `span` runs the function directly and\n   * `log` is a no-op. No null-checking needed.\n   *\n   * ```ts\n   * execute: async ({ userId }, { observe }) => {\n   *   observe.log('info', 'fetching user', { userId })\n   *   return observe.span('fetch user', () => fetch(`/api/users/${userId}`))\n   * }\n   * ```\n   */\n  observe: ToolObserve;\n}\n\nexport interface ToolAction<\n  TSchemaIn,\n  TSchemaOut,\n  TSuspend = unknown,\n  TResume = unknown,\n  TContext extends ToolExecutionContext<TSuspend, TResume, any> = ToolExecutionContext<TSuspend, TResume>,\n  TId extends string = string,\n  TRequestContext extends Record<string, any> | unknown = unknown,\n> {\n  id: TId;\n  description: string;\n  inputSchema?: PublicSchema<TSchemaIn>;\n  outputSchema?: PublicSchema<TSchemaOut>;\n  suspendSchema?: PublicSchema<TSuspend>;\n  resumeSchema?: PublicSchema<TResume>;\n  /**\n   * Optional schema for validating request context values.\n   * When provided, the request context will be validated against this schema before tool execution.\n   * If validation fails, a validation error is returned instead of executing the tool.\n   */\n  requestContextSchema?: PublicSchema<TRequestContext>;\n  /**\n   * Optional MCP-specific properties.\n   * Only populated when the tool is being used in an MCP context.\n   */\n  mcp?: MCPToolProperties;\n  /**\n   * Optional function to transform tool output before returning to the model.\n   * Receives the raw tool output and returns a transformed representation.\n   * Passed through from the original tool definition.\n   */\n  toModelOutput?: (output: TSchemaOut) => unknown;\n  /**\n   * Optional target-aware transform for tool payloads that leave runtime.\n   *\n   * Runtime execution still receives raw inputs and outputs. These transforms\n   * are used by display and transcript serializers to avoid exposing internal\n   * payload fields.\n   */\n  transform?: ToolPayloadTransform<TSchemaIn, TSchemaOut>;\n  // Execute signature with unified context type\n  // First parameter: raw input data (validated against inputSchema)\n  // Second parameter: unified execution context with all metadata\n  // Returns: The expected output, a validation error, or void when the tool\n  // suspends via `context.agent?.suspend?.(...)` / `context.workflow?.suspend?.(...)`.\n  // When `suspend` has been called, the tool runtime skips output validation\n  // (see `Tool.execute` in tool.ts), so returning `undefined` after `suspend`\n  // is the supported idiom (e.g. `return await suspend(...)`).\n  // Note: When no outputSchema is provided, returns any to allow property access\n  // Note: For outputSchema, we use the input type because Zod transforms are applied during validation\n  // Note: { error?: never } enables inline type narrowing with 'error' in result checks\n  execute?: (inputData: TSchemaIn, context: TContext) => Promise<TSchemaOut | ValidationError | void>;\n  mastra?: Mastra;\n  /**\n   * Whether the tool requires explicit user approval before execution.\n   * Pass `true` to always require approval, or a function evaluated per-call\n   * with the tool input (and optional request context/workspace) to require\n   * approval conditionally.\n   */\n  requireApproval?:\n    | boolean\n    | ((\n        input: TSchemaIn,\n        ctx?: { requestContext?: Record<string, unknown>; workspace?: Workspace },\n      ) => boolean | Promise<boolean>);\n  /**\n   * Enables strict tool input generation for providers that support it.\n   * When enabled, supported providers will attempt to generate arguments\n   * that exactly match the tool schema.\n   */\n  strict?: boolean;\n  /**\n   * Provider-specific options passed to the model when this tool is used.\n   * Keys are provider names (e.g., 'anthropic', 'openai'), values are provider-specific configs.\n   * @example\n   * ```typescript\n   * providerOptions: {\n   *   anthropic: {\n   *     cacheControl: { type: 'ephemeral' }\n   *   }\n   * }\n   * ```\n   */\n  providerOptions?: Record<string, Record<string, unknown>>;\n  /**\n   * Metadata identifying this tool as originating from an MCP server.\n   * Set automatically by the MCP client when creating tools.\n   * Used by CoreToolBuilder to create MCP_TOOL_CALL spans instead of TOOL_CALL spans.\n   */\n  mcpMetadata?: McpMetadata;\n  /**\n   * Examples of valid tool inputs. Each example contains an `input` object\n   * showing what valid arguments look like.\n   * Passed through to the AI SDK which forwards them to model providers\n   * that support input examples (e.g., Anthropic's `input_examples` beta feature).\n   */\n  inputExamples?: Array<{ input: Record<string, unknown> }>;\n  onInputStart?: (options: ToolCallOptions) => void | PromiseLike<void>;\n  onInputDelta?: (\n    options: {\n      inputTextDelta: string;\n    } & ToolCallOptions,\n  ) => void | PromiseLike<void>;\n  onInputAvailable?: (\n    options: {\n      input: TSchemaIn;\n    } & ToolCallOptions,\n  ) => void | PromiseLike<void>;\n  onOutput?: (\n    options: {\n      output: TSchemaOut;\n      toolName: string;\n    } & Omit<ToolCallOptions, 'messages'>,\n  ) => void | PromiseLike<void>;\n  /**\n   * Background task configuration for this tool.\n   * When enabled, the tool can be executed in the background while the agent conversation continues.\n   */\n  background?: ToolBackgroundConfig;\n}\n","import type { Schema } from '@internal/ai-v6';\nimport type { ProviderDefinedTool, ToolExecutionOptions } from '@internal/external-types';\nimport {\n  OpenAIReasoningSchemaCompatLayer,\n  OpenAISchemaCompatLayer,\n  GoogleSchemaCompatLayer,\n  AnthropicSchemaCompatLayer,\n  DeepSeekSchemaCompatLayer,\n  MetaSchemaCompatLayer,\n  applyCompatLayer,\n  convertZodSchemaToAISDKSchema,\n  jsonSchema,\n} from '@mastra/schema-compat';\nimport type { JSONSchema7Definition } from 'json-schema';\nimport { z } from 'zod/v4';\nimport { MastraFGAPermissions } from '../../auth/ee';\nimport { backgroundOverrideJsonSchema, backgroundOverrideZodSchema } from '../../background-tasks';\nimport { MastraBase } from '../../base';\nimport { ErrorCategory, MastraError, ErrorDomain } from '../../error';\nimport type { Mastra } from '../../mastra';\nimport { SpanType, wrapMastra, EntityType, getOrCreateSpan, createObservabilityContext } from '../../observability';\nimport type { AnySpan } from '../../observability';\nimport { executeWithContext } from '../../observability/utils';\nimport { RequestContext } from '../../request-context';\nimport { isStandardSchemaWithJSON, toStandardSchema, standardSchemaToJSONSchema } from '../../schema';\nimport type { StandardSchemaWithJSON } from '../../schema';\nimport { getNeedsApprovalFn, isVercelTool, isProviderDefinedTool } from '../../tools/toolchecks';\nimport type { ToolOptions } from '../../utils';\nimport { safeStringify } from '../../utils';\nimport { isZodObject, safeExtendZodObject } from '../../utils/zod-utils';\n\nimport type { SuspendOptions } from '../../workflows';\nimport { ToolStream } from '../stream';\nimport type {\n  CoreTool,\n  McpMetadata,\n  MastraToolInvocationOptions,\n  NeedsApprovalFn,\n  ToolAction,\n  VercelTool,\n  VercelToolV5,\n} from '../types';\nimport { noopObserve } from '../types';\nimport { validateToolInput, validateToolOutput, validateToolSuspendData } from '../validation';\n\n/**\n * Merge two RequestContexts so non-serializable values survive the evented\n * workflow engine's toJSON/reconstruct cycle.\n *\n * The evented engine serialises the RequestContext via `toJSON()` when\n * publishing workflow events.  Values that fail `JSON.stringify` (functions,\n * objects with circular references — e.g. the `harness` context) are silently\n * dropped.  The reconstructed RC handed to steps is therefore *degraded*.\n *\n * Tools, however, also hold a reference to the *original* RC captured during\n * tool conversion (the \"closure\" RC).  By merging both — exec first, then\n * closure on top — keys that survived serialisation are preserved while\n * non-serializable keys from the closure (like `harness`) are restored.\n */\nfunction mergeRequestContexts(\n  closureRC: RequestContext | undefined,\n  execRC: RequestContext | undefined,\n): RequestContext {\n  if (!closureRC && !execRC) return new RequestContext();\n  if (!closureRC) return execRC instanceof RequestContext ? execRC : new RequestContext();\n  if (!execRC || !(execRC instanceof RequestContext) || execRC.size() === 0) return closureRC;\n\n  const merged = new RequestContext();\n  // Start with the evented engine's serialised snapshot\n  for (const [key, value] of execRC.entries()) {\n    merged.set(key, value);\n  }\n  // Overlay closure values — restores non-serializable keys and ensures the\n  // authoritative (non-degraded) copy wins for keys present in both.\n  for (const [key, value] of closureRC.entries()) {\n    merged.set(key, value);\n  }\n  return merged;\n}\n\n/**\n * Types that can be converted to Mastra tools.\n * Includes provider-defined tools from external packages via ProviderDefinedTool.\n */\nexport type ToolToConvert = VercelTool | ToolAction<any, any, any> | VercelToolV5 | ProviderDefinedTool;\nexport type LogType = 'tool' | 'toolset' | 'client-tool';\n\ninterface LogOptions {\n  agentName?: string;\n  toolName: string;\n  type?: 'tool' | 'toolset' | 'client-tool';\n}\n\ninterface LogMessageOptions {\n  start: string;\n  error: string;\n  logData: Record<string, unknown>;\n}\n\n/**\n * Detect Zod v4 schemas. Zod v3 stores the type name as `_def.typeName`\n * (e.g. \"ZodObject\"); Zod v4 stores it as `_def.type` (e.g. \"object\"). We\n * cannot use `instanceof` here because both Zod versions may be loaded in the\n * same process and the prototype identity is not guaranteed.\n */\nfunction isZodV4Schema(schema: unknown): boolean {\n  const def = (schema as any)?._def;\n  return !!def && typeof def.type === 'string' && !def.typeName;\n}\n\n/**\n * Build a Standard Schema that:\n *  - exposes the spliced JSON Schema (with `_background`/`suspendedToolRunId`/\n *    `resumeData` properties added) so provider compat layers see the override\n *    fields when serializing the tool to an LLM, and\n *  - delegates runtime `validate` to the *original* schema so Zod v3\n *    `.transform()` / `.default()` / `.refine()` and other Standard Schema\n *    parsing behavior still run before `execute()` sees the args.\n *\n * Injected override keys (`_background`, `suspendedToolRunId`, `resumeData`)\n * are stripped from the input before delegating, then merged back into the\n * validated value so the inner `execute()` still receives them — matching the\n * Zod v4 `.extend()` path's behavior.\n *\n * If the original schema has no `~standard.validate` (e.g. a raw JSON Schema\n * with no Standard Schema wrapper), fall back to validating against the\n * spliced JSON Schema directly.\n */\nfunction buildJsonOverrideSchema(\n  originalSchema: unknown,\n  splicedJsonSchema: JSONSchema7Definition,\n  injectedKeys: readonly string[],\n): StandardSchemaWithJSON {\n  const fallback = toStandardSchema(splicedJsonSchema as any);\n  const original = originalSchema as { '~standard'?: { validate?: (v: unknown) => any } } | undefined;\n  const originalValidate = original?.['~standard']?.validate?.bind(original['~standard']);\n\n  // Standard Schema for *just* the injected override fields, so we can validate\n  // malformed override payloads (e.g. `_background: { enabled: \"yes\" }`) before\n  // merging them into the result. Matches the Zod v4 `.extend()` path's\n  // behavior, which validates these fields as part of the object.\n  // See https://github.com/mastra-ai/mastra/pull/16915#discussion_r3282600679\n  const splicedProperties =\n    splicedJsonSchema && typeof splicedJsonSchema === 'object' && 'properties' in splicedJsonSchema\n      ? ((splicedJsonSchema.properties ?? {}) as Record<string, JSONSchema7Definition>)\n      : {};\n  const injectedProperties: Record<string, JSONSchema7Definition> = {};\n  for (const key of injectedKeys) {\n    if (splicedProperties[key] !== undefined) injectedProperties[key] = splicedProperties[key];\n  }\n  const injectedValidator = toStandardSchema({\n    type: 'object',\n    properties: injectedProperties,\n    additionalProperties: false,\n  } as any);\n\n  const stripInjected = (input: unknown) => {\n    if (!input || typeof input !== 'object' || Array.isArray(input)) return { stripped: input, injected: {} };\n    const injected: Record<string, unknown> = {};\n    const stripped: Record<string, unknown> = {};\n    for (const [k, v] of Object.entries(input as Record<string, unknown>)) {\n      if (injectedKeys.includes(k)) injected[k] = v;\n      else stripped[k] = v;\n    }\n    return { stripped, injected };\n  };\n\n  const validate = (input: unknown) => {\n    const { stripped, injected } = stripInjected(input);\n\n    const baseResult = originalValidate\n      ? (originalValidate(stripped) as\n          | { value: unknown }\n          | { issues: readonly unknown[] }\n          | Promise<{ value: unknown } | { issues: readonly unknown[] }>)\n      : fallback['~standard'].validate(stripped);\n\n    const injectedResult = injectedValidator['~standard'].validate(injected);\n\n    const combine = (\n      base: { value: unknown } | { issues: readonly unknown[] },\n      inj: { value: unknown } | { issues: readonly unknown[] },\n    ) => {\n      const baseIssues = 'issues' in base ? (base.issues ?? []) : [];\n      const injIssues = 'issues' in inj ? (inj.issues ?? []) : [];\n      if (baseIssues.length || injIssues.length) {\n        return { issues: [...baseIssues, ...injIssues] };\n      }\n      const baseValue = (base as { value: unknown }).value;\n      const injValue = (inj as { value: unknown }).value;\n      if (baseValue && typeof baseValue === 'object' && !Array.isArray(baseValue)) {\n        const injMerged =\n          injValue && typeof injValue === 'object' && !Array.isArray(injValue)\n            ? (injValue as Record<string, unknown>)\n            : injected;\n        return { value: { ...(baseValue as Record<string, unknown>), ...injMerged } };\n      }\n      return base;\n    };\n\n    const baseIsPromise = baseResult && typeof (baseResult as Promise<unknown>).then === 'function';\n    const injIsPromise = injectedResult && typeof (injectedResult as Promise<unknown>).then === 'function';\n    if (baseIsPromise || injIsPromise) {\n      return Promise.all([baseResult, injectedResult]).then(([b, i]) =>\n        combine(\n          b as { value: unknown } | { issues: readonly unknown[] },\n          i as { value: unknown } | { issues: readonly unknown[] },\n        ),\n      );\n    }\n    return combine(\n      baseResult as { value: unknown } | { issues: readonly unknown[] },\n      injectedResult as { value: unknown } | { issues: readonly unknown[] },\n    );\n  };\n\n  return {\n    '~standard': {\n      version: 1,\n      vendor: 'mastra-json-override',\n      validate,\n      jsonSchema: fallback['~standard'].jsonSchema,\n    },\n  } as StandardSchemaWithJSON;\n}\n\nexport class CoreToolBuilder extends MastraBase {\n  private originalTool: ToolToConvert;\n  private options: ToolOptions;\n  private logType?: LogType;\n\n  constructor(input: {\n    originalTool: ToolToConvert;\n    options: ToolOptions;\n    logType?: LogType;\n    autoResumeSuspendedTools?: boolean;\n    backgroundTaskEnabled?: boolean;\n  }) {\n    super({ name: 'CoreToolBuilder' });\n    this.originalTool = input.originalTool;\n    this.options = input.options;\n    this.logType = input.logType;\n\n    // Only inject the `_background` override schema for tools that are actually\n    // eligible for background execution — otherwise every user tool's input\n    // schema would be mutated with a v4 Zod field, which breaks v3-authored\n    // tools (keyValidator._parse crashes in schema-compat validation).\n    const isBackgroundEligible = !!input.backgroundTaskEnabled;\n    const isResumableTool =\n      input.autoResumeSuspendedTools ||\n      (this.originalTool as unknown as ToolAction<any, any>).id?.startsWith('agent-') ||\n      (this.originalTool as unknown as ToolAction<any, any>).id?.startsWith('workflow-');\n\n    if (!isVercelTool(this.originalTool) && !isProviderDefinedTool(this.originalTool)) {\n      if (isBackgroundEligible || isResumableTool) {\n        let schema = this.originalTool.inputSchema;\n        if (typeof schema === 'function') {\n          schema = schema();\n        }\n        if (!schema) {\n          schema = z.object({});\n        }\n\n        // Preferred path: when the user's input schema is a Zod v4 ZodObject\n        // (the common case for tools authored with `zod` / `zod/v4`), keep using\n        // `.extend()`. This preserves the exact JSON Schema shape that existing\n        // provider compat layers + LLM recordings expect.\n        //\n        // Fallback path: for everything else (Zod v3 ZodObject, raw JSON Schema,\n        // `JsonSchemaWrapper`, etc.) splice the override fields directly into a\n        // JSON Schema. Mixing a Zod v4 wrapper (`backgroundOverrideZodSchema`,\n        // `.nullable()`, `.optional()`) into a Zod v3 ZodObject's `.shape` is\n        // what triggered the original crash:\n        //   `TypeError: keyValidator._parse is not a function`.\n        if (isZodObject(schema) && isZodV4Schema(schema)) {\n          let nextSchema: z.ZodObject<any> = schema as z.ZodObject<any>;\n          if (isBackgroundEligible) {\n            nextSchema = safeExtendZodObject(nextSchema, {\n              _background: backgroundOverrideZodSchema,\n            });\n          }\n          if (isResumableTool) {\n            nextSchema = safeExtendZodObject(nextSchema, {\n              suspendedToolRunId: z.string().describe('The runId of the suspended tool').nullable().optional(),\n              resumeData: z\n                .any()\n                .describe('The resumeData object created from the resumeSchema of suspended tool')\n                .optional(),\n            });\n          }\n          this.originalTool.inputSchema = nextSchema;\n        } else {\n          // Normalize to Standard Schema, extract JSON Schema, splice overrides.\n          const standardSchema = isStandardSchemaWithJSON(schema) ? schema : toStandardSchema(schema);\n          const jsonSchema = standardSchemaToJSONSchema(standardSchema, { io: 'input' });\n\n          if (jsonSchema && typeof jsonSchema === 'object' && jsonSchema.type === 'object') {\n            const properties: Record<string, JSONSchema7Definition> = { ...(jsonSchema.properties ?? {}) };\n            const injectedKeys: string[] = [];\n\n            if (isBackgroundEligible) {\n              properties._background = backgroundOverrideJsonSchema;\n              injectedKeys.push('_background');\n            }\n            if (isResumableTool) {\n              // Match the pre-PR JSON Schema shape so existing provider compat\n              // layers + LLM recordings collapse it identically.\n              properties.suspendedToolRunId = {\n                type: ['string', 'null'],\n                description: 'The runId of the suspended tool',\n              };\n              properties.resumeData = {\n                description: 'The resumeData object created from the resumeSchema of suspended tool',\n              };\n              injectedKeys.push('suspendedToolRunId', 'resumeData');\n            }\n\n            // Preserve the original schema's runtime validator (Zod v3\n            // `.transform()` / `.default()` / `.refine()` etc.) while exposing\n            // the spliced JSON Schema for provider serialization. See\n            // https://github.com/mastra-ai/mastra/pull/16915#discussion_r3282520408\n            this.originalTool.inputSchema = buildJsonOverrideSchema(\n              schema,\n              { ...jsonSchema, properties },\n              injectedKeys,\n            );\n          }\n        }\n      }\n    }\n  }\n\n  // Helper to get parameters based on tool type\n  private getParameters = () => {\n    if (isVercelTool(this.originalTool)) {\n      // Handle both 'parameters' (v4) and 'inputSchema' (v5) properties\n      // Also handle case where the schema is a function that returns a schema\n      let schema =\n        this.originalTool.parameters ??\n        ('inputSchema' in this.originalTool ? (this.originalTool as any).inputSchema : undefined) ??\n        z.object({});\n\n      // If schema is a function, call it to get the actual schema\n      if (typeof schema === 'function') {\n        schema = schema();\n      }\n\n      return schema;\n    }\n\n    // For Mastra tools, inputSchema might also be a function\n    let schema = this.originalTool.inputSchema;\n\n    if (isStandardSchemaWithJSON(schema)) {\n      return schema;\n    }\n\n    // If schema is a function, call it to get the actual schema\n    if (typeof schema === 'function') {\n      schema = schema();\n    }\n\n    return schema;\n  };\n\n  private getOutputSchema = () => {\n    if ('outputSchema' in this.originalTool) {\n      let schema = this.originalTool.outputSchema;\n\n      if (isStandardSchemaWithJSON(schema)) {\n        return schema;\n      }\n\n      // If schema is a function, call it to get the actual schema\n      if (typeof schema === 'function') {\n        schema = schema();\n      }\n\n      return schema;\n    }\n\n    return null;\n  };\n\n  private getResumeSchema = () => {\n    if ('resumeSchema' in this.originalTool) {\n      let schema = this.originalTool.resumeSchema;\n\n      // If schema is a function, call it to get the actual schema\n      if (typeof schema === 'function') {\n        schema = schema();\n      }\n\n      return schema;\n    }\n    return null;\n  };\n\n  private getSuspendSchema = () => {\n    if ('suspendSchema' in this.originalTool) {\n      let schema = this.originalTool.suspendSchema;\n\n      // If schema is a function, call it to get the actual schema\n      if (typeof schema === 'function') {\n        schema = schema();\n      }\n\n      return schema;\n    }\n    return null;\n  };\n\n  // For provider-defined tools, we need to include all required properties\n  // AI SDK v5 uses type: 'provider-defined', AI SDK v6 uses type: 'provider'\n  private buildProviderTool(tool: ToolToConvert): (CoreTool & { id: `${string}.${string}` }) | undefined {\n    if (\n      'type' in tool &&\n      (tool.type === 'provider-defined' || tool.type === 'provider') &&\n      'id' in tool &&\n      typeof tool.id === 'string' &&\n      tool.id.includes('.')\n    ) {\n      // Get schema directly from provider-defined tool (v4 uses parameters, v5 uses inputSchema)\n      let parameters: unknown =\n        'parameters' in tool ? tool.parameters : 'inputSchema' in tool ? (tool as any).inputSchema : undefined;\n\n      // If schema is a function, call it to get the actual schema\n      if (typeof parameters === 'function') {\n        parameters = parameters();\n      }\n\n      // Get output schema directly from provider-defined tool\n      let outputSchema: unknown = 'outputSchema' in tool ? (tool as any).outputSchema : undefined;\n\n      // If schema is a function, call it to get the actual schema\n      if (typeof outputSchema === 'function') {\n        outputSchema = outputSchema();\n      }\n\n      // Convert parameters to AI SDK Schema format\n      let processedParameters;\n      if (parameters !== undefined && parameters !== null) {\n        if (typeof parameters === 'object' && 'jsonSchema' in parameters) {\n          // Already in AI SDK Schema format\n          processedParameters = parameters;\n        } else if (isStandardSchemaWithJSON(parameters)) {\n          // StandardSchemaWithJSON - extract the JSON schema and wrap it\n          // Use input since parameters represent tool input\n          const jsonSchema = standardSchemaToJSONSchema(parameters, { io: 'input' });\n          processedParameters = { jsonSchema };\n        } else {\n          // Assume Zod schema - convert to AI SDK Schema\n          processedParameters = convertZodSchemaToAISDKSchema(parameters as any);\n        }\n      } else {\n        // No schema provided - create default empty object schema for AI SDK v1 compatibility\n        // OpenAI requires at minimum type: \"object\" even for tools without parameters\n        processedParameters = {\n          jsonSchema: {\n            type: 'object',\n            properties: {},\n            additionalProperties: false,\n          },\n        };\n      }\n\n      // Convert output schema to AI SDK Schema format if present\n      let processedOutputSchema;\n      if (outputSchema !== undefined && outputSchema !== null) {\n        if (typeof outputSchema === 'object' && 'jsonSchema' in outputSchema) {\n          // Already in AI SDK Schema format\n          processedOutputSchema = outputSchema;\n        } else if (isStandardSchemaWithJSON(outputSchema)) {\n          // StandardSchemaWithJSON - extract the JSON schema and wrap it\n          const jsonSchema = standardSchemaToJSONSchema(outputSchema);\n          processedOutputSchema = { jsonSchema };\n        } else {\n          // Assume Zod schema - convert to AI SDK Schema\n          processedOutputSchema = convertZodSchemaToAISDKSchema(outputSchema as any);\n        }\n      }\n\n      return {\n        ...(processedOutputSchema ? { outputSchema: processedOutputSchema } : {}),\n        type: 'provider-defined' as const,\n        id: tool.id as `${string}.${string}`,\n        // V5 SDK factories set a hardcoded `name` (e.g. \"web_search\" for\n        // anthropic.web_search_20250305). Preserve it so that when this tool\n        // is later used with a V6 provider, the bidirectional toolNameMapping\n        // resolves the correct model-facing name instead of the versioned ID.\n        ...('name' in tool && typeof tool.name === 'string' ? { name: tool.name } : {}),\n        args: ('args' in this.originalTool ? this.originalTool.args : {}) as Record<string, unknown>,\n        description: tool.description,\n        parameters: processedParameters,\n        execute: this.originalTool.execute\n          ? this.createExecute(\n              this.originalTool,\n              { ...this.options, description: this.originalTool.description },\n              this.logType,\n            )\n          : undefined,\n        toModelOutput: 'toModelOutput' in this.originalTool ? this.originalTool.toModelOutput : undefined,\n        transform: 'transform' in this.originalTool ? this.originalTool.transform : undefined,\n        inputExamples: 'inputExamples' in this.originalTool ? this.originalTool.inputExamples : undefined,\n      } as unknown as (CoreTool & { id: `${string}.${string}` }) | undefined;\n    }\n\n    return undefined;\n  }\n\n  private createLogMessageOptions({ agentName, toolName, type }: LogOptions): LogMessageOptions {\n    const toolType = type === 'toolset' ? 'toolset' : 'tool';\n    return {\n      start: `Executing ${toolType}`,\n      error: `Failed ${toolType} execution`,\n      logData: { agent: agentName, tool: toolName },\n    };\n  }\n\n  private createExecute(tool: ToolToConvert, options: ToolOptions, logType?: 'tool' | 'toolset' | 'client-tool') {\n    // don't add memory, mastra, or tracing context to logging (tracingContext may contain sensitive observability credentials)\n    const {\n      logger,\n      mastra: _mastra,\n      memory: _memory,\n      requestContext,\n      model,\n      tracingContext: _tracingContext,\n      tracingPolicy: _tracingPolicy,\n      ...rest\n    } = options;\n    const logModelObject = {\n      modelId: model?.modelId,\n      provider: model?.provider,\n      specificationVersion: model?.specificationVersion,\n    };\n\n    const { start, logData } = this.createLogMessageOptions({\n      agentName: options.agentName,\n      toolName: options.name,\n      type: logType,\n    });\n\n    // Extract MCP metadata once with proper typing to avoid repeated unsafe casts\n    const mcpMeta =\n      !isVercelTool(tool) && 'mcpMetadata' in tool ? (tool as { mcpMetadata?: McpMetadata }).mcpMetadata : undefined;\n\n    const execFunction = async (args: unknown, execOptions: MastraToolInvocationOptions, toolSpan?: AnySpan) => {\n      try {\n        let result;\n        let suspendData = null;\n\n        if (isVercelTool(tool)) {\n          // Handle Vercel tools (AI SDK tools)\n          result = await executeWithContext({\n            span: toolSpan,\n            fn: async () => tool?.execute?.(args, execOptions as ToolExecutionOptions),\n          });\n        } else {\n          // Handle Mastra tools - wrap mastra instance with tracing context for context propagation\n\n          /**\n           * MASTRA INSTANCE TYPES IN TOOL EXECUTION:\n           *\n           * Full Mastra & MastraPrimitives (has getAgent, getWorkflow, etc.):\n           * - Auto-generated workflow tools from agent.listWorkflows()\n           * - These get this.#mastra directly and can be wrapped\n           *\n           * MastraPrimitives only (limited interface):\n           * - Memory tools (from memory.listTools())\n           * - Assigned tools (agent.tools)\n           * - Toolset tools (from toolsets)\n           * - Client tools (passed as tools in generate/stream options)\n           * - These get mastraProxy and have limited functionality\n           *\n           * TODO: Consider providing full Mastra instance to more tool types for enhanced functionality\n           */\n          // Wrap mastra with tracing context - wrapMastra will handle whether it's a full instance or primitives\n          const wrappedMastra = options.mastra ? wrapMastra(options.mastra, { currentSpan: toolSpan }) : options.mastra;\n\n          const resumeSchema = this.getResumeSchema();\n          // Pass raw args as first parameter, context as second\n          // Properly structure context based on execution source\n          const baseContext = {\n            threadId: options.threadId,\n            resourceId: options.resourceId,\n            mastra: wrappedMastra,\n            memory: options.memory,\n            runId: options.runId,\n            requestContext: mergeRequestContexts(options.requestContext, execOptions.requestContext),\n            actor: execOptions.actor,\n            // Workspace for file operations and command execution\n            // Execution-time workspace (from prepareStep/processInputStep) takes precedence over build-time workspace\n            workspace: execOptions.workspace ?? options.workspace,\n            // Browser for web automation (lazily initialized on first use)\n            browser: options.browser,\n            observe: execOptions.observe ?? noopObserve,\n            writer: new ToolStream(\n              {\n                prefix: 'tool',\n                callId: execOptions.toolCallId,\n                name: options.name,\n                runId: options.runId!,\n              },\n              options.outputWriter || execOptions.outputWriter,\n            ),\n            ...createObservabilityContext({ currentSpan: toolSpan }),\n            abortSignal: execOptions.abortSignal,\n            suspend: (args: any, suspendOptions?: SuspendOptions) => {\n              suspendData = args;\n              const newSuspendOptions = {\n                ...(suspendOptions ?? {}),\n                resumeSchema:\n                  suspendOptions?.resumeSchema ??\n                  (resumeSchema\n                    ? JSON.stringify(standardSchemaToJSONSchema(toStandardSchema(resumeSchema), { io: 'input' }))\n                    : undefined),\n              };\n              return execOptions.suspend?.(args, newSuspendOptions);\n            },\n            resumeData: execOptions.resumeData,\n          };\n\n          // Check if this is agent execution\n          // Agent execution takes precedence over workflow execution because agents may\n          // use workflows internally for their agentic loop\n          // Note: AI SDK v4 doesn't pass toolCallId/messages, so we also check for agentName and threadId\n          const isAgentExecution =\n            (execOptions.toolCallId && execOptions.messages) ||\n            (options.agentName && options.threadId && !options.workflowId);\n\n          // Check if this is workflow execution (has workflow properties in options)\n          // Only consider it workflow execution if it's NOT agent execution\n          const isWorkflowExecution = !isAgentExecution && (options.workflow || options.workflowId);\n\n          let toolContext;\n          if (isAgentExecution) {\n            // Nest agent-specific properties under 'agent' key\n            // Do NOT include workflow context even if workflow properties exist\n            // (agents use workflows internally but tools should see agent context)\n            const { suspend, resumeData, threadId, resourceId, ...restBaseContext } = baseContext;\n            toolContext = {\n              ...restBaseContext,\n              agent: {\n                agentId: options.agentId || '',\n                toolCallId: execOptions.toolCallId || '',\n                messages: execOptions.messages || [],\n                suspend,\n                resumeData,\n                threadId,\n                resourceId,\n                outputWriter: execOptions.outputWriter,\n                flushMessages: execOptions.flushMessages,\n              },\n            };\n          } else if (isWorkflowExecution) {\n            // Nest workflow-specific properties under 'workflow' key\n            const { suspend, resumeData, ...restBaseContext } = baseContext;\n            toolContext = {\n              ...restBaseContext,\n              workflow: options.workflow || {\n                runId: options.runId,\n                workflowId: options.workflowId,\n                state: options.state,\n                setState: options.setState,\n                suspend,\n                resumeData,\n              },\n            };\n          } else if (execOptions.mcp) {\n            // MCP execution context\n            toolContext = {\n              ...baseContext,\n              mcp: execOptions.mcp,\n            };\n          } else {\n            // Direct execution or unknown context\n            toolContext = baseContext;\n          }\n\n          const resumeData = execOptions.resumeData;\n\n          if (resumeData) {\n            const resumeValidation = validateToolInput(resumeSchema, resumeData, options.name);\n            if (resumeValidation.error) {\n              logger?.warn(resumeValidation.error.message);\n              toolSpan?.end({ output: resumeValidation.error, attributes: { success: false } });\n              return resumeValidation.error as any;\n            }\n          }\n\n          result = await executeWithContext({ span: toolSpan, fn: async () => tool?.execute?.(args, toolContext) });\n        }\n\n        if (suspendData) {\n          const suspendSchema = this.getSuspendSchema();\n          const suspendValidation = validateToolSuspendData(suspendSchema, suspendData, options.name);\n          if (suspendValidation.error) {\n            logger?.warn(suspendValidation.error.message);\n            toolSpan?.end({ output: suspendValidation.error, attributes: { success: false } });\n            return suspendValidation.error as any;\n          }\n        }\n\n        // Skip validation if suspend was called without a result\n        const shouldSkipValidation = typeof result === 'undefined' && !!suspendData;\n        if (shouldSkipValidation) {\n          toolSpan?.end({ output: result, attributes: { success: true } });\n          return result;\n        }\n\n        // Validate output for Vercel/AI SDK tools which don't have built-in validation\n        // Mastra tools handle their own validation in Tool.execute() which properly\n        // applies Zod transforms (e.g., .transform(), .pipe()) to the output\n        if (isVercelTool(tool)) {\n          const outputSchema = this.getOutputSchema();\n          const outputValidation = validateToolOutput(outputSchema, result, options.name, false);\n          if (outputValidation.error) {\n            logger?.warn(outputValidation.error.message);\n            toolSpan?.end({ output: outputValidation.error, attributes: { success: false } });\n            return outputValidation.error;\n          }\n          result = outputValidation.data;\n        }\n\n        // Return result (validated for Vercel tools, already validated for Mastra tools)\n        toolSpan?.end({ output: result, attributes: { success: true } });\n        return result;\n      } catch (error) {\n        toolSpan?.error({ error: error as Error, attributes: { success: false } });\n        throw error;\n      }\n    };\n\n    return async (args: unknown, execOptions?: MastraToolInvocationOptions) => {\n      let logger = options.logger || this.logger;\n\n      // Create tool span early so validation failures are always observable.\n      // Prefer execution-time tracingContext (passed at runtime for VNext methods)\n      // Fall back to build-time context for Legacy methods (AI SDK v4 doesn't support passing custom options)\n      const tracingContext = execOptions?.tracingContext || options.tracingContext;\n      const toolRequestContext = execOptions?.requestContext ?? options.requestContext;\n      const toolSpan = getOrCreateSpan({\n        type: mcpMeta ? SpanType.MCP_TOOL_CALL : SpanType.TOOL_CALL,\n        name: mcpMeta ? `mcp_tool: '${options.name}' on '${mcpMeta.serverName}'` : `tool: '${options.name}'`,\n        input: args,\n        entityType: EntityType.TOOL,\n        entityId: options.name,\n        entityName: options.name,\n        attributes: mcpMeta\n          ? {\n              mcpServer: mcpMeta.serverName,\n              serverVersion: mcpMeta.serverVersion,\n              toolDescription: options.description,\n            }\n          : {\n              toolDescription: options.description,\n              toolType: logType || 'tool',\n            },\n        tracingPolicy: options.tracingPolicy,\n        tracingContext: tracingContext,\n        requestContext: toolRequestContext,\n        mastra: options.mastra && 'observability' in options.mastra ? (options.mastra as Mastra) : undefined,\n      });\n\n      const fgaProvider = (options.mastra as any)?.getServer?.()?.fga;\n      const user = toolRequestContext?.get('user');\n      if (fgaProvider) {\n        const { getAgentToolFGAResourceId, getMCPToolFGAResourceId, getStandaloneToolFGAResourceId, requireFGA } =\n          await import('../../auth/ee/fga-check');\n        const toolResourceId = mcpMeta?.serverName\n          ? getMCPToolFGAResourceId(mcpMeta.serverName, options.name)\n          : options.agentId\n            ? getAgentToolFGAResourceId(options.agentId, options.name)\n            : getStandaloneToolFGAResourceId(options.name);\n        await requireFGA({\n          fgaProvider,\n          user,\n          resource: { type: 'tool', id: toolResourceId },\n          permission: MastraFGAPermissions.TOOLS_EXECUTE,\n          requestContext: toolRequestContext,\n          actor: execOptions?.actor,\n          context: {\n            resourceId: options.resourceId,\n          },\n          metadata: {\n            toolName: options.name,\n            agentId: options.agentId,\n            agentName: options.agentName,\n            runId: options.runId,\n            threadId: options.threadId,\n            executionResourceId: options.resourceId,\n            mcpMetadata: mcpMeta,\n          },\n        });\n      }\n\n      try {\n        logger.debug(start, { ...logData, ...rest, model: logModelObject, args });\n\n        // Validate input parameters if schema exists\n        // Use the processed schema for validation if available, otherwise fall back to original\n        const parameters = this.getParameters();\n        const { data, error } = validateToolInput(parameters, args, options.name);\n        //suspendedToolRunId is only required when resumeData is provided\n        const suspendedToolRunIdErrToIgnore =\n          error?.message?.includes('suspendedToolRunId: Required') && !(args as Record<string, unknown>)?.resumeData;\n        if (error && !suspendedToolRunIdErrToIgnore) {\n          logger.warn('Tool input validation failed', { ...logData, validationError: error.message });\n          toolSpan?.end({ output: error, attributes: { success: false } });\n          return error;\n        }\n        // Use validated/transformed data\n        args = data;\n\n        // there is a small delay in stream output so we add an immediate to ensure the stream is ready\n        return await new Promise((resolve, reject) => {\n          setImmediate(async () => {\n            try {\n              const result = await execFunction(args, execOptions!, toolSpan);\n              resolve(result);\n            } catch (err) {\n              reject(err);\n            }\n          });\n        });\n      } catch (err) {\n        const mastraError = new MastraError(\n          {\n            id: 'TOOL_EXECUTION_FAILED',\n            domain: ErrorDomain.TOOL,\n            category: ErrorCategory.USER,\n            details: {\n              errorMessage: String(err),\n              argsJson: safeStringify(args),\n              model: model?.modelId ?? '',\n            },\n          },\n          err,\n        );\n        toolSpan?.error({ error: mastraError, attributes: { success: false } });\n        logger.trackException(mastraError, { ...logData, ...rest, model: logModelObject, args });\n        throw mastraError;\n      }\n    };\n  }\n\n  buildV5() {\n    const builtTool = this.build();\n\n    if (!builtTool.parameters) {\n      throw new Error('Tool parameters are required');\n    }\n\n    const base = {\n      ...builtTool,\n      inputSchema: builtTool.parameters,\n      onInputStart: 'onInputStart' in this.originalTool ? this.originalTool.onInputStart : undefined,\n      onInputDelta: 'onInputDelta' in this.originalTool ? this.originalTool.onInputDelta : undefined,\n      onInputAvailable: 'onInputAvailable' in this.originalTool ? this.originalTool.onInputAvailable : undefined,\n      onOutput: 'onOutput' in this.originalTool ? this.originalTool.onOutput : undefined,\n    };\n\n    // For provider-defined tools, exclude execute and add name as per v5 spec\n    if (builtTool.type === 'provider-defined') {\n      const { execute, parameters, ...rest } = base;\n      // Prefer the preserved provider name (e.g. \"web_search\" from V5 SDK\n      // factories) over the ID-derived name (e.g. \"web_search_20250305\").\n      const name =\n        ('name' in builtTool && typeof builtTool.name === 'string' ? builtTool.name : null) ||\n        builtTool.id.split('.')[1] ||\n        builtTool.id;\n      return {\n        ...rest,\n        type: builtTool.type,\n        id: builtTool.id,\n        name,\n        args: builtTool.args,\n      } as VercelToolV5;\n    }\n\n    return base as VercelToolV5;\n  }\n\n  build(): CoreTool {\n    const providerTool = this.buildProviderTool(this.originalTool);\n    if (providerTool) {\n      return providerTool;\n    }\n    const model = this.options.model;\n\n    const schemaCompatLayers = [];\n\n    if (model) {\n      // Respect the model's own capability flag; do not disable it based solely on specificationVersion.\n      const supportsStructuredOutputs =\n        'supportsStructuredOutputs' in model ? (model.supportsStructuredOutputs ?? false) : false;\n\n      const modelInfo = {\n        modelId: model.modelId,\n        supportsStructuredOutputs,\n        provider: model.provider,\n      };\n\n      schemaCompatLayers.push(\n        new OpenAIReasoningSchemaCompatLayer(modelInfo),\n        new OpenAISchemaCompatLayer(modelInfo),\n        new GoogleSchemaCompatLayer(modelInfo),\n        new AnthropicSchemaCompatLayer(modelInfo),\n        new DeepSeekSchemaCompatLayer(modelInfo),\n        new MetaSchemaCompatLayer(modelInfo),\n      );\n    }\n\n    const originalSchema = this.getParameters();\n    let processedInputSchema: Schema | undefined;\n\n    if (originalSchema) {\n      if (isStandardSchemaWithJSON(originalSchema)) {\n        // Find the first applicable compatibility layer\n        const applicableLayer = schemaCompatLayers.find(layer => layer.shouldApply());\n\n        let schemaToUse: StandardSchemaWithJSON;\n        if (applicableLayer) {\n          schemaToUse = applicableLayer.processToCompatSchema(originalSchema as any);\n        } else {\n          schemaToUse = toStandardSchema(originalSchema);\n        }\n\n        processedInputSchema = jsonSchema(\n          standardSchemaToJSONSchema(schemaToUse, {\n            io: 'input',\n          }),\n          {\n            validate: (value: unknown) => {\n              const result = schemaToUse['~standard'].validate(value);\n              // standard-schema validate may return a Promise\n              if (result instanceof Promise) {\n                return result.then(r => {\n                  if ('issues' in r && r.issues) {\n                    return {\n                      success: false as const,\n                      error: new Error(r.issues.map((i: any) => i.message).join(', ')),\n                    };\n                  }\n                  return { success: true as const, value: (r as { value: unknown }).value };\n                });\n              }\n              // standard-schema returns { value } on success or { issues } on failure,\n              // but AI SDK expects { success: boolean, value/error }\n              if ('issues' in result && result.issues) {\n                return {\n                  success: false as const,\n                  error: new Error(result.issues.map((i: any) => i.message).join(', ')),\n                };\n              }\n              return { success: true as const, value: (result as { value: unknown }).value };\n            },\n          },\n        );\n      } else {\n        processedInputSchema = applyCompatLayer({\n          schema: originalSchema,\n          compatLayers: schemaCompatLayers,\n          mode: 'aiSdkSchema',\n        });\n      }\n    }\n\n    const outputSchema = this.getOutputSchema();\n    let processedOutputSchema;\n\n    if (outputSchema) {\n      if (isStandardSchemaWithJSON(outputSchema)) {\n        processedOutputSchema = standardSchemaToJSONSchema(outputSchema, { io: 'output' });\n      } else {\n        processedOutputSchema = applyCompatLayer({\n          schema: outputSchema,\n          compatLayers: [],\n          mode: 'aiSdkSchema',\n        });\n      }\n    }\n\n    // Map AI SDK's needsApproval to our requireApproval\n    // needsApproval can be boolean or a function that takes input and returns boolean\n    let requireApproval = false;\n    let needsApprovalFn: NeedsApprovalFn | undefined;\n\n    if (typeof this.options.requireApproval === 'function') {\n      requireApproval = true;\n      needsApprovalFn = this.options.requireApproval;\n    } else if (typeof this.options.requireApproval === 'boolean') {\n      requireApproval = this.options.requireApproval;\n      needsApprovalFn = undefined;\n    }\n\n    if (isVercelTool(this.originalTool) && 'needsApproval' in this.originalTool) {\n      const needsApproval = (this.originalTool as any).needsApproval;\n      if (typeof needsApproval === 'boolean') {\n        requireApproval = needsApproval;\n        needsApprovalFn = undefined;\n      } else if (typeof needsApproval === 'function') {\n        // Store the function to evaluate it per-call\n        needsApprovalFn = needsApproval;\n        // Set requireApproval to true so the tool-call-step knows to check the function\n        requireApproval = true;\n      }\n    }\n\n    // Preserve a needsApprovalFn that was attached directly to the tool instance\n    // (e.g. MCP tools wrap a server-level `requireToolApproval` function and set\n    // `needsApprovalFn` on the tool while keeping `requireApproval` as a boolean).\n    // The branches above only derive needsApprovalFn from options/AI SDK shapes, so\n    // without this it would be dropped during conversion and conditional approval\n    // would silently fall back to the boolean flag.\n    const instanceNeedsApprovalFn = getNeedsApprovalFn(this.originalTool);\n    if (!needsApprovalFn && instanceNeedsApprovalFn) {\n      needsApprovalFn = instanceNeedsApprovalFn;\n      // Ensure the tool-call-step knows to evaluate the function per call.\n      requireApproval = true;\n    }\n\n    const definition = {\n      type: 'function' as const,\n      description: this.originalTool.description,\n      requireApproval,\n      needsApprovalFn,\n      hasSuspendSchema: !!this.getSuspendSchema(),\n      execute: this.originalTool.execute\n        ? this.createExecute(\n            this.originalTool,\n            { ...this.options, description: this.originalTool.description },\n            this.logType,\n          )\n        : undefined,\n    };\n\n    return {\n      ...definition,\n      id: 'id' in this.originalTool ? this.originalTool.id : undefined,\n      parameters: processedInputSchema ?? z.object({}),\n      outputSchema: processedOutputSchema,\n      strict: 'strict' in this.originalTool ? this.originalTool.strict : undefined,\n      providerOptions: 'providerOptions' in this.originalTool ? this.originalTool.providerOptions : undefined,\n      mcp: 'mcp' in this.originalTool ? this.originalTool.mcp : undefined,\n      toModelOutput: 'toModelOutput' in this.originalTool ? this.originalTool.toModelOutput : undefined,\n      transform: 'transform' in this.originalTool ? this.originalTool.transform : undefined,\n      inputExamples: 'inputExamples' in this.originalTool ? this.originalTool.inputExamples : undefined,\n      onInputStart: 'onInputStart' in this.originalTool ? this.originalTool.onInputStart : undefined,\n      onInputDelta: 'onInputDelta' in this.originalTool ? this.originalTool.onInputDelta : undefined,\n      onInputAvailable: 'onInputAvailable' in this.originalTool ? this.originalTool.onInputAvailable : undefined,\n      onOutput: 'onOutput' in this.originalTool ? this.originalTool.onOutput : undefined,\n      // Preserve tool-level background config so the agentic loop can pick it up\n      // from the converted CoreTool at dispatch time.\n      backgroundConfig: this.options.backgroundConfig,\n    } as unknown as CoreTool;\n  }\n}\n","import { createHash } from 'node:crypto';\nimport type { CoreMessage } from '@internal/ai-sdk-v4';\nimport { jsonSchemaToZod } from '@mastra/schema-compat/json-to-zod';\nimport { z } from 'zod/v4';\nimport type { MastraPrimitives } from './action';\nimport type { ToolsInput } from './agent';\nimport type { ToolBackgroundConfig } from './background-tasks';\nimport type { MastraBrowser } from './browser/browser';\nimport { ErrorCategory, ErrorDomain, MastraError } from './error';\nimport type { MastraLanguageModel, MastraLegacyLanguageModel } from './llm/model/shared.types';\nimport type { IMastraLogger } from './logger';\nimport type { Mastra } from './mastra';\nimport type { AiMessageType, MastraMemory } from './memory';\nimport type { ObservabilityContext, TracingPolicy } from './observability';\nimport type { RequestContext } from './request-context';\nimport type { CoreTool, VercelTool, VercelToolV5 } from './tools';\nimport { Tool } from './tools/tool';\nimport { CoreToolBuilder } from './tools/tool-builder/builder';\nimport type { ToolToConvert } from './tools/tool-builder/builder';\nimport { isVercelTool } from './tools/toolchecks';\nimport type { OutputWriter } from './workflows/types';\nimport type { Workspace } from './workspace/workspace';\n\n// Re-export Zod utilities for external use (isZodType is defined locally below)\nexport { getZodTypeName, getZodDef, isZodArray, isZodObject } from './utils/zod-utils';\nexport { fetchWithRetry } from './utils/fetchWithRetry';\nexport type { FetchWithRetryOptions } from './utils/fetchWithRetry';\n\nexport const delay = (ms: number) => new Promise(resolve => setTimeout(resolve, ms));\n\n/**\n * Safely JSON-stringifies a value, replacing circular references with \"[Circular]\".\n * Uses a stack-based approach so shared (non-circular) references are preserved.\n */\nexport function safeStringify(value: unknown, space?: string | number): string {\n  const stack: unknown[] = [];\n  return JSON.stringify(\n    value,\n    function (this: unknown, _key: string, val: unknown) {\n      if (typeof val === 'bigint') return val.toString();\n      if (val !== null && typeof val === 'object') {\n        // Trim the stack: pop entries that are no longer ancestors of the current path.\n        // `this` is the parent object containing the current key.\n        while (stack.length > 0 && stack[stack.length - 1] !== this) {\n          stack.pop();\n        }\n        if (stack.includes(val)) return '[Circular]';\n        stack.push(val);\n      }\n      return val;\n    },\n    space,\n  );\n}\n\n/**\n * Returns a JSON-serializable copy of a value by stripping circular references.\n * If the value is already serializable, returns it unchanged (no cloning overhead).\n */\nexport function ensureSerializable(value: unknown): unknown {\n  if (value === null || typeof value !== 'object') return value;\n\n  try {\n    JSON.stringify(value);\n    return value;\n  } catch {\n    return JSON.parse(safeStringify(value));\n  }\n}\n\n/**\n * Checks if a value is a plain object (not an array, function, Date, RegExp, etc.)\n */\nfunction isPlainObject(value: unknown): value is Record<string, unknown> {\n  if (value === null || typeof value !== 'object') return false;\n  const proto = Object.getPrototypeOf(value);\n  return proto === Object.prototype || proto === null;\n}\n\n/**\n * Deep merges two objects, recursively merging nested plain objects.\n * Arrays, functions, and other non-plain objects are replaced (not merged).\n */\nexport function deepMerge<T extends object = object>(target: T, source: Partial<T>): T {\n  const output = { ...target };\n\n  if (!source) return output;\n\n  Object.keys(source).forEach(key => {\n    const targetValue = (output as Record<string, unknown>)[key];\n    const sourceValue = (source as Record<string, unknown>)[key];\n\n    // Only deep merge if both values are plain objects\n    if (isPlainObject(targetValue) && isPlainObject(sourceValue)) {\n      (output as Record<string, unknown>)[key] = deepMerge(targetValue, sourceValue);\n    } else if (sourceValue !== undefined) {\n      // For arrays, functions, primitives, and other non-plain objects: replace\n      (output as Record<string, unknown>)[key] = sourceValue;\n    }\n  });\n\n  return output;\n}\n\n/**\n * Deep equality comparison for comparing two values.\n * Handles primitives, arrays, objects, and Date instances.\n */\nexport function deepEqual(a: unknown, b: unknown): boolean {\n  // Handle identical references and primitives\n  if (a === b) return true;\n\n  // Handle null/undefined\n  if (a == null || b == null) return a === b;\n\n  // Handle different types\n  if (typeof a !== typeof b) return false;\n\n  // Handle arrays\n  if (Array.isArray(a) && Array.isArray(b)) {\n    if (a.length !== b.length) return false;\n    return a.every((item, index) => deepEqual(item, b[index]));\n  }\n\n  // Handle dates (must check before generic objects since Date is also an object)\n  if (a instanceof Date && b instanceof Date) {\n    return a.getTime() === b.getTime();\n  }\n\n  // Handle objects (after Date check to avoid treating Dates as plain objects)\n  if (typeof a === 'object' && typeof b === 'object') {\n    const aObj = a as Record<string, unknown>;\n    const bObj = b as Record<string, unknown>;\n    const aKeys = Object.keys(aObj);\n    const bKeys = Object.keys(bObj);\n\n    if (aKeys.length !== bKeys.length) return false;\n\n    // Verify that bObj has the same keys as aObj before comparing values\n    return aKeys.every(key => Object.prototype.hasOwnProperty.call(bObj, key) && deepEqual(aObj[key], bObj[key]));\n  }\n\n  return false;\n}\n\n/**\n * Generate an empty object from a JSON Schema definition.\n * Accepts both a JSON string and a pre-parsed object.\n * Recursively initializes nested object properties and respects default values.\n */\nexport function generateEmptyFromSchema(schema: string | Record<string, unknown>): Record<string, unknown> {\n  try {\n    const parsedSchema = typeof schema === 'string' ? JSON.parse(schema) : schema;\n    if (!parsedSchema || parsedSchema.type !== 'object' || !parsedSchema.properties) return {};\n    const obj: Record<string, unknown> = {};\n    for (const [key, prop] of Object.entries<Record<string, unknown>>(\n      parsedSchema.properties as Record<string, Record<string, unknown>>,\n    )) {\n      if (prop.default !== undefined) {\n        obj[key] =\n          typeof prop.default === 'object' && prop.default !== null\n            ? JSON.parse(JSON.stringify(prop.default))\n            : prop.default;\n      } else if (prop.type === 'object' && prop.properties) {\n        obj[key] = generateEmptyFromSchema(prop);\n      } else if (prop.type === 'object') {\n        obj[key] = {};\n      } else if (prop.type === 'string') {\n        obj[key] = '';\n      } else if (prop.type === 'array') {\n        obj[key] = [];\n      } else if (prop.type === 'number' || prop.type === 'integer') {\n        obj[key] = 0;\n      } else if (prop.type === 'boolean') {\n        obj[key] = false;\n      } else {\n        obj[key] = null;\n      }\n    }\n    return obj;\n  } catch {\n    return {};\n  }\n}\n\nexport interface TagMaskOptions {\n  /** Called when masking begins */\n  onStart?: () => void;\n  /** Called when masking ends */\n  onEnd?: () => void;\n  /** Called for each chunk that is masked */\n  onMask?: (chunk: string) => void;\n}\n\n/**\n * Transforms a stream by masking content between XML tags.\n * @param stream Input stream to transform\n * @param tag Tag name to mask between (e.g. for <foo>...</foo>, use 'foo')\n * @param options Optional configuration for masking behavior\n */\nexport async function* maskStreamTags(\n  stream: AsyncIterable<string>,\n  tag: string,\n  options: TagMaskOptions = {},\n): AsyncIterable<string> {\n  const { onStart, onEnd, onMask } = options;\n  const openTag = `<${tag}>`;\n  const closeTag = `</${tag}>`;\n\n  let buffer = '';\n  let fullContent = '';\n  let isMasking = false;\n  let isBuffering = false;\n\n  // used for checking in chunks that include tags or partial tags + some other non-tag text\n  // eg: \"o <tag_name\" or \"name> w\", can trim before-start to get \"<tag_name\" or after-end to get \"name>\"\n  const trimOutsideDelimiter = (text: string, delimiter: string, trim: 'before-start' | 'after-end') => {\n    if (!text.includes(delimiter)) {\n      return text;\n    }\n\n    const parts = text.split(delimiter);\n\n    if (trim === `before-start`) {\n      return `${delimiter}${parts[1]}`;\n    }\n\n    return `${parts[0]}${delimiter}`;\n  };\n\n  // Helper to check if text starts with pattern (ignoring whitespace)\n  // When checking partial tags: startsWith(buffer, openTag) checks if buffer could be start of tag\n  // When checking full tags: startsWith(chunk, openTag) checks if chunk starts with full tag\n  const startsWith = (text: string, pattern: string) => {\n    // check start of opening tag\n    if (pattern.includes(openTag.substring(0, 3))) {\n      // our pattern for checking the start is always based on xml-like tags\n      // if the pattern looks like our opening tag and the pattern also includes\n      // some other chunked text before it, we just wanted to check the xml part of the pattern\n      pattern = trimOutsideDelimiter(pattern, `<`, `before-start`);\n    }\n\n    return text.trim().startsWith(pattern.trim());\n  };\n\n  for await (const chunk of stream) {\n    fullContent += chunk;\n\n    if (isBuffering) buffer += chunk;\n\n    const chunkHasTag = startsWith(chunk, openTag);\n    const bufferHasTag = !chunkHasTag && isBuffering && startsWith(openTag, buffer);\n\n    let toYieldBeforeMaskedStartTag = ``;\n    // Check if we should start masking chunks\n    if (!isMasking && (chunkHasTag || bufferHasTag)) {\n      isMasking = true;\n      isBuffering = false;\n\n      // check if the buffered text includes text before the start tag. ex \"o <tag_name\", \"o\" should be yielded and not masked\n      const taggedTextToMask = trimOutsideDelimiter(buffer, `<`, `before-start`);\n      if (taggedTextToMask !== buffer.trim()) {\n        toYieldBeforeMaskedStartTag = buffer.replace(taggedTextToMask, ``);\n      }\n\n      buffer = '';\n      onStart?.();\n    }\n\n    // Check if we should start buffering (looks like part of the opening tag but it's not the full <tag> yet eg <ta - could be <table> but we don't know yet)\n    if (!isMasking && !isBuffering && startsWith(openTag, chunk) && chunk.trim() !== '') {\n      isBuffering = true;\n      buffer += chunk;\n      continue;\n    }\n\n    // We're buffering, need to check again if our buffer has deviated from the opening <tag> eg <tag2>\n    if (isBuffering && buffer && !startsWith(openTag, buffer)) {\n      yield buffer;\n      buffer = '';\n      isBuffering = false;\n      continue;\n    }\n\n    // Check if we should stop masking chunks (since the content includes the closing </tag>)\n    if (isMasking && fullContent.includes(closeTag)) {\n      onMask?.(chunk);\n      onEnd?.();\n      isMasking = false;\n      const lastFullContent = fullContent;\n      fullContent = ``; // reset to handle streams with multiple full tags that have text inbetween\n\n      // check to see if we have a partial chunk outside the close tag. if we do we need to yield it so it isn't swallowed with the masked text\n      const textUntilEndTag = trimOutsideDelimiter(lastFullContent, closeTag, 'after-end');\n      if (textUntilEndTag !== lastFullContent) {\n        yield lastFullContent.replace(textUntilEndTag, ``);\n      }\n\n      continue;\n    }\n\n    // We're currently masking chunks inside a <tag>\n    if (isMasking) {\n      onMask?.(chunk);\n      // in the case that there was a chunk that included a tag to mask and some other text, ex \"o <tag_name\" we need to still yield the\n      // text before the tag (\"o \") so it's not swallowed with the masked text\n      if (toYieldBeforeMaskedStartTag) {\n        yield toYieldBeforeMaskedStartTag;\n      }\n      continue;\n    }\n\n    // default yield the chunk\n    yield chunk;\n  }\n}\n\n/**\n * Resolve serialized zod output - This function takes the string output ot the `jsonSchemaToZod` function\n * and instantiates the zod object correctly.\n *\n * @param schema - serialized zod object\n * @returns resolved zod object\n */\nexport function resolveSerializedZodOutput(schema: string): z.ZodType {\n  // Creates and immediately executes a new function that takes 'z' as a parameter\n  // The function body is a string that returns the serialized zod schema\n  // When executed with the 'z' parameter, it reconstructs the zod schema in the current context\n  return Function('z', `\"use strict\";return (${schema});`)(z);\n}\n\nexport interface ToolOptions extends Partial<ObservabilityContext> {\n  name: string;\n  runId?: string;\n  threadId?: string;\n  resourceId?: string;\n  logger?: IMastraLogger;\n  description?: string;\n  mastra?: (Mastra & MastraPrimitives) | MastraPrimitives;\n  requestContext: RequestContext;\n  tracingPolicy?: TracingPolicy;\n  memory?: MastraMemory;\n  agentName?: string;\n  agentId?: string;\n  model?: MastraLanguageModel | MastraLegacyLanguageModel;\n  /**\n   * Optional async writer used to stream tool output chunks back to the caller. Tools should treat this as fire-and-forget I/O.\n   */\n  outputWriter?: OutputWriter;\n  requireApproval?:\n    | boolean\n    | ((\n        input: any,\n        ctx?: { requestContext?: Record<string, unknown>; workspace?: Workspace },\n      ) => boolean | Promise<boolean>);\n  // Workflow-specific properties\n  workflow?: any;\n  workflowId?: string;\n  state?: any;\n  setState?: (state: any) => void;\n  /**\n   * Workspace available for tool execution. When provided, tools can access\n   * workspace.filesystem and workspace.sandbox for file operations and command execution.\n   */\n  workspace?: Workspace;\n  backgroundConfig?: ToolBackgroundConfig;\n  /**\n   * Browser available for tool execution. When provided, tools can access\n   * browser capabilities for web automation, screenshots, and data extraction.\n   */\n  browser?: MastraBrowser;\n}\n\n/**\n * Checks if a value is a Zod type\n * @param value - The value to check\n * @returns True if the value is a Zod type, false otherwise\n */\nexport function isZodType(value: unknown): value is z.ZodType {\n  // Check if it's a Zod schema by looking for common Zod properties and methods\n  return (\n    typeof value === 'object' &&\n    value !== null &&\n    '_def' in value &&\n    'parse' in value &&\n    typeof (value as any).parse === 'function' &&\n    'safeParse' in value &&\n    typeof (value as any).safeParse === 'function'\n  );\n}\n\n// Helper function to create a deterministic hash\nfunction createDeterministicId(input: string): string {\n  return createHash('sha256').update(input).digest('hex').slice(0, 8); // Take first 8 characters for a shorter but still unique ID\n}\n\n/**\n * Sets the properties for a Vercel Tool, including an ID and inputSchema\n * @param tool - The tool to set the properties for\n * @returns The tool with the properties set\n */\nfunction setVercelToolProperties(tool: VercelTool) {\n  // Check if the tool already has inputSchema (v5 format)\n  // If it does, use it directly (it might be a function)\n  // Otherwise, convert the parameters to inputSchema\n  const inputSchema = 'inputSchema' in tool ? tool.inputSchema : convertVercelToolParameters(tool);\n\n  const toolId = !('id' in tool)\n    ? tool.description\n      ? `tool-${createDeterministicId(tool.description)}`\n      : `tool-${Math.random().toString(36).substring(2, 9)}`\n    : tool.id;\n\n  return {\n    ...tool,\n    id: toolId,\n    inputSchema,\n  };\n}\n\n/**\n * Ensures a tool has an ID and inputSchema by generating one if not present\n * @param tool - The tool to ensure has an ID and inputSchema\n * @returns The tool with an ID and inputSchema\n */\nexport function ensureToolProperties(tools: ToolsInput): ToolsInput {\n  const toolsWithProperties = Object.keys(tools).reduce<ToolsInput>((acc, key) => {\n    const tool = tools?.[key];\n    if (tool) {\n      // Check if the tool is a plain function (not a Tool instance or Vercel tool)\n      // This catches the common mistake of passing a tool factory function instead of the tool itself\n      // We need to cast to unknown first since ToolsInput doesn't include functions in its type,\n      // but users can still pass functions at runtime which causes silent failures\n      if (typeof tool === 'function' && !((tool as unknown) instanceof Tool) && !isVercelTool(tool)) {\n        throw new MastraError({\n          id: 'TOOL_INVALID_FORMAT',\n          domain: ErrorDomain.TOOL,\n          category: ErrorCategory.USER,\n          text: `Tool \"${key}\" is not a valid tool format. Tools must be created using createTool() or be a valid Vercel AI SDK tool. Received a function.`,\n        });\n      }\n\n      if (isVercelTool(tool)) {\n        acc[key] = setVercelToolProperties(tool) as VercelTool;\n      } else {\n        acc[key] = tool;\n      }\n    }\n    return acc;\n  }, {});\n\n  return toolsWithProperties;\n}\n\nfunction convertVercelToolParameters(tool: VercelTool): z.ZodType {\n  // If the tool is a Vercel Tool, check if the parameters are already a zod object\n  // If not, convert the parameters to a zod object using jsonSchemaToZod\n  // Handle case where parameters (or inputSchema in v5) is a function that returns a schema\n  let schema = tool.parameters ?? z.object({});\n\n  // If schema is a function, call it to get the actual schema\n  if (typeof schema === 'function') {\n    schema = schema();\n  }\n\n  return isZodType(schema) ? schema : resolveSerializedZodOutput(jsonSchemaToZod(schema));\n}\n\n/**\n * Converts a Vercel Tool or Mastra Tool into a CoreTool format\n * @param originalTool - The tool to convert (either VercelTool or ToolAction)\n * @param options - Tool options including Mastra-specific settings\n * @param logType - Type of tool to log (tool or toolset)\n * @returns A CoreTool that can be used by the system\n */\nexport function makeCoreTool(\n  originalTool: ToolToConvert,\n  options: ToolOptions,\n  logType?: 'tool' | 'toolset' | 'client-tool',\n  autoResumeSuspendedTools?: boolean,\n  backgroundTaskEnabled?: boolean,\n): CoreTool {\n  return new CoreToolBuilder({\n    originalTool,\n    options,\n    logType,\n    autoResumeSuspendedTools,\n    backgroundTaskEnabled,\n  }).build();\n}\n\nexport function makeCoreToolV5(\n  originalTool: ToolToConvert,\n  options: ToolOptions,\n  logType?: 'tool' | 'toolset' | 'client-tool',\n  autoResumeSuspendedTools?: boolean,\n  backgroundTaskEnabled?: boolean,\n): VercelToolV5 {\n  return new CoreToolBuilder({\n    originalTool,\n    options,\n    logType,\n    autoResumeSuspendedTools,\n    backgroundTaskEnabled,\n  }).buildV5();\n}\n\n/**\n * Creates a proxy for a Mastra instance to handle deprecated properties\n * @param mastra - The Mastra instance to proxy\n * @param logger - The logger to use for warnings\n * @returns A proxy for the Mastra instance\n */\nexport function createMastraProxy({ mastra, logger }: { mastra: Mastra; logger: IMastraLogger }) {\n  return new Proxy(mastra, {\n    get(target, prop) {\n      const hasProp = Reflect.has(target, prop);\n\n      if (hasProp) {\n        const value = Reflect.get(target, prop);\n        const isFunction = typeof value === 'function';\n        if (isFunction) {\n          return value.bind(target);\n        }\n        return value;\n      }\n\n      if (prop === 'logger') {\n        logger.warn(\"Please use 'getLogger' instead, logger is deprecated\");\n        return Reflect.apply(target.getLogger, target, []);\n      }\n\n      if (prop === 'storage') {\n        logger.warn(\"Please use 'getStorage' instead, storage is deprecated\");\n        return Reflect.get(target, 'storage');\n      }\n\n      if (prop === 'agents') {\n        logger.warn(\"Please use 'listAgents' instead, agents is deprecated\");\n        return Reflect.apply(target.listAgents, target, []);\n      }\n\n      if (prop === 'tts') {\n        logger.warn(\"Please use 'getTTS' instead, tts is deprecated\");\n        return Reflect.apply(target.getTTS, target, []);\n      }\n\n      if (prop === 'vectors') {\n        logger.warn(\"Please use 'getVectors' instead, vectors is deprecated\");\n        return Reflect.apply(target.getVectors, target, []);\n      }\n\n      if (prop === 'memory') {\n        logger.warn(\"Please use 'getMemory' instead, memory is deprecated\");\n        return Reflect.get(target, 'memory');\n      }\n\n      return Reflect.get(target, prop);\n    },\n  });\n}\n\nexport function checkEvalStorageFields(traceObject: any, logger?: IMastraLogger) {\n  const missingFields = [];\n  if (!traceObject.input) missingFields.push('input');\n  if (!traceObject.output) missingFields.push('output');\n  if (!traceObject.agentName) missingFields.push('agent_name');\n  if (!traceObject.metricName) missingFields.push('metric_name');\n  if (!traceObject.instructions) missingFields.push('instructions');\n  if (!traceObject.globalRunId) missingFields.push('global_run_id');\n  if (!traceObject.runId) missingFields.push('run_id');\n\n  if (missingFields.length > 0) {\n    if (logger) {\n      logger.warn('Skipping evaluation storage due to missing required fields', {\n        missingFields,\n        runId: traceObject.runId,\n        agentName: traceObject.agentName,\n      });\n    } else {\n      console.warn('Skipping evaluation storage due to missing required fields', {\n        missingFields,\n        runId: traceObject.runId,\n        agentName: traceObject.agentName,\n      });\n    }\n    return false;\n  }\n\n  return true;\n}\n\n// lifted from https://github.com/vercel/ai/blob/main/packages/ai/core/prompt/detect-prompt-type.ts#L27\nfunction detectSingleMessageCharacteristics(\n  message: any,\n): 'has-ui-specific-parts' | 'has-core-specific-parts' | 'message' | 'other' {\n  if (\n    typeof message === 'object' &&\n    message !== null &&\n    (message.role === 'function' || // UI-only role\n      message.role === 'data' || // UI-only role\n      'toolInvocations' in message || // UI-specific field\n      'parts' in message || // UI-specific field\n      'experimental_attachments' in message)\n  ) {\n    return 'has-ui-specific-parts';\n  } else if (\n    typeof message === 'object' &&\n    message !== null &&\n    'content' in message &&\n    (Array.isArray(message.content) || // Core messages can have array content\n      'experimental_providerMetadata' in message ||\n      'providerOptions' in message)\n  ) {\n    return 'has-core-specific-parts';\n  } else if (\n    typeof message === 'object' &&\n    message !== null &&\n    'role' in message &&\n    'content' in message &&\n    typeof message.content === 'string' &&\n    ['system', 'user', 'assistant', 'tool'].includes(message.role)\n  ) {\n    return 'message';\n  } else {\n    return 'other';\n  }\n}\n\nexport function isUiMessage(message: CoreMessage | AiMessageType): message is AiMessageType {\n  return detectSingleMessageCharacteristics(message) === `has-ui-specific-parts`;\n}\nexport function isCoreMessage(message: CoreMessage | AiMessageType): message is CoreMessage {\n  return [`has-core-specific-parts`, `message`].includes(detectSingleMessageCharacteristics(message));\n}\n\n/** Represents a validated SQL identifier (e.g., table or column name). */\ntype SqlIdentifier = string & { __brand: 'SqlIdentifier' };\n/** Represents a validated dot-separated SQL field key. */\ntype FieldKey = string & { __brand: 'FieldKey' };\n\nconst SQL_IDENTIFIER_PATTERN = /^[a-zA-Z_][a-zA-Z0-9_]*$/;\n\n/**\n * Parses and returns a valid SQL identifier (such as a table or column name).\n * The identifier must:\n *   - Start with a letter (a-z, A-Z) or underscore (_)\n *   - Contain only letters, numbers, or underscores\n *   - Be at most 63 characters long\n *\n * @param name - The identifier string to parse.\n * @param kind - Optional label for error messages (e.g., 'table name').\n * @returns The validated identifier as a branded type.\n * @throws {Error} If the identifier does not conform to SQL naming rules.\n *\n * @example\n * const id = parseSqlIdentifier('my_table'); // Ok\n * parseSqlIdentifier('123table'); // Throws error\n */\nexport function parseSqlIdentifier(name: string, kind = 'identifier'): SqlIdentifier {\n  if (!SQL_IDENTIFIER_PATTERN.test(name) || name.length > 63) {\n    throw new Error(\n      `Invalid ${kind}: ${name}. Must start with a letter or underscore, contain only letters, numbers, or underscores, and be at most 63 characters long.`,\n    );\n  }\n  return name as SqlIdentifier;\n}\n\n/**\n * Parses and returns a valid dot-separated SQL field key (e.g., 'user.profile.name').\n * Each segment must:\n *   - Start with a letter (a-z, A-Z) or underscore (_)\n *   - Contain only letters, numbers, or underscores\n *   - Be at most 63 characters long\n *\n * @param key - The dot-separated field key string to parse.\n * @returns The validated field key as a branded type.\n * @throws {Error} If any segment of the key is invalid.\n *\n * @example\n * const key = parseFieldKey('user_profile.name'); // Ok\n * parseFieldKey('user..name'); // Throws error\n * parseFieldKey('user.123name'); // Throws error\n */\nexport function parseFieldKey(key: string): FieldKey {\n  if (!key) throw new Error('Field key cannot be empty');\n  const segments = key.split('.');\n  for (const segment of segments) {\n    if (!SQL_IDENTIFIER_PATTERN.test(segment) || segment.length > 63) {\n      throw new Error(`Invalid field key segment: ${segment} in ${key}`);\n    }\n  }\n  return key as FieldKey;\n}\n\n/**\n * Removes specific keys from an object.\n * @param obj - The original object\n * @param keysToOmit - Keys to exclude from the returned object\n * @returns A new object with the specified keys removed\n */\nexport function omitKeys<T extends Record<string, any>>(obj: T, keysToOmit: string[]): Partial<T> {\n  return Object.fromEntries(Object.entries(obj).filter(([key]) => !keysToOmit.includes(key))) as Partial<T>;\n}\n\n/**\n * Selectively extracts specific fields from an object using dot notation.\n * Does not error if fields don't exist - simply omits them from the result.\n * @param obj - The source object to extract fields from\n * @param fields - Array of field paths (supports dot notation like 'output.text')\n * @returns New object containing only the specified fields\n */\nexport function selectFields(obj: any, fields: string[]): any {\n  if (!obj || typeof obj !== 'object') {\n    return obj;\n  }\n\n  const result: any = {};\n\n  for (const field of fields) {\n    const value = getNestedValue(obj, field);\n    if (value !== undefined) {\n      setNestedValue(result, field, value);\n    }\n  }\n\n  return result;\n}\n\n/**\n * Gets a nested value from an object using dot notation\n * @param obj - Source object\n * @param path - Dot notation path (e.g., 'output.text')\n * @returns The value at the path, or undefined if not found\n */\nexport function getNestedValue(obj: any, path: string): any {\n  return path.split('.').reduce((current, key) => {\n    return current && typeof current === 'object' ? current[key] : undefined;\n  }, obj);\n}\n\n/**\n * Sets a nested value in an object using dot notation\n * @param obj - Target object\n * @param path - Dot notation path (e.g., 'output.text')\n * @param value - Value to set\n */\nexport function setNestedValue(obj: any, path: string, value: any): void {\n  const keys = path.split('.');\n  const lastKey = keys.pop();\n  if (!lastKey) return;\n\n  // Validate every segment up-front so we never walk or assign through\n  // a prototype-mutating key like \"__proto__\", \"constructor\", or \"prototype\".\n  for (const key of keys) {\n    if (key === '__proto__' || key === 'constructor' || key === 'prototype') {\n      return;\n    }\n  }\n  if (lastKey === '__proto__' || lastKey === 'constructor' || lastKey === 'prototype') {\n    return;\n  }\n\n  let current = obj;\n  for (const key of keys) {\n    const existing = Object.prototype.hasOwnProperty.call(current, key) ? current[key] : undefined;\n    if (existing === null || typeof existing !== 'object') {\n      // Use a null-prototype object so intermediate containers cannot be\n      // used as a prototype-pollution sink even if a sanitizer check is\n      // bypassed upstream.\n      const container = Object.create(null);\n      Object.defineProperty(current, key, { value: container, writable: true, enumerable: true, configurable: true });\n    }\n    current = current[key];\n  }\n\n  Object.defineProperty(current, lastKey, { value, writable: true, enumerable: true, configurable: true });\n}\n\nexport const removeUndefinedValues = (obj: Record<string, any>) => {\n  return Object.fromEntries(Object.entries(obj).filter(([_, value]) => value !== undefined));\n};\n"]}