Tools - OpenRouter SDK

The tool() Helper

The tool() function creates type-safe tools with Zod schema validation:

import { OpenRouter, tool } from '@openrouter/agent';
import { z } from 'zod';

const weatherTool = tool({
  name: 'get_weather',
  description: 'Get the current weather for a location',
  inputSchema: z.object({
    location: z.string().describe('City name, e.g., "San Francisco, CA"'),
  }),
  outputSchema: z.object({
    temperature: z.number(),
    conditions: z.string(),
  }),
  execute: async (params) => {
    // params is typed as { location: string }
    const weather = await fetchWeather(params.location);
    return {
      temperature: weather.temp,
      conditions: weather.description,
    };
  },
});

Tool Types

The SDK supports four types of tools, automatically detected from your configuration:

Regular Tools

Standard tools with an execute function:

const calculatorTool = tool({
  name: 'calculate',
  description: 'Perform a mathematical calculation',
  inputSchema: z.object({
    expression: z.string().describe('Math expression like "2 + 2"'),
  }),
  outputSchema: z.object({
    result: z.number(),
  }),
  execute: async (params) => {
    const result = eval(params.expression); // Use a safer eval in production
    return { result };
  },
});

Generator Tools

Tools that yield progress updates during execution. Add eventSchema to enable generator mode:

const searchTool = tool({
  name: 'search_database',
  description: 'Search documents with progress updates',
  inputSchema: z.object({
    query: z.string(),
    limit: z.number().default(10),
  }),
  // eventSchema triggers generator mode
  eventSchema: z.object({
    progress: z.number().min(0).max(100),
    message: z.string(),
  }),
  outputSchema: z.object({
    results: z.array(z.string()),
    totalFound: z.number(),
  }),
  // execute is now an async generator
  execute: async function* (params) {
    yield { progress: 0, message: 'Starting search...' };

    const results = [];
    for (let i = 0; i < 5; i++) {
      yield { progress: (i + 1) * 20, message: `Searching batch ${i + 1}...` };
      results.push(...await searchBatch(params.query, i));
    }

    // Final yield is the output
    yield { progress: 100, message: 'Complete!' };

    // Return the final result (or yield it as last value)
    return {
      results: results.slice(0, params.limit),
      totalFound: results.length,
    };
  },
});

Progress events are streamed to consumers via getToolStream() and getFullResponsesStream().

Manual Tools

Tools without automatic execution - you handle the tool calls yourself:

const manualTool = tool({
  name: 'send_email',
  description: 'Send an email (requires user confirmation)',
  inputSchema: z.object({
    to: z.string().email(),
    subject: z.string(),
    body: z.string(),
  }),
  execute: false, // Manual handling required
});

Use getToolCalls() to retrieve manual tool calls for processing.

Human-in-the-Loop (HITL) Tools

HITL tools extend manual-tool semantics with two sync-or-async hooks that let you decide per call whether to respond programmatically or pause for a human:

onToolCalled — fires when the model invokes the tool. Return a value to feed the model directly (like a regular execute), or return null to pause the loop like a manual tool. The caller resumes later by supplying a function_call_output item.
onResponseReceived — optional. Fires on a later turn when an incoming function_call_output matches a prior call of this tool (by callId → function_call.name). It receives the caller-supplied raw result and returns the value sent to the model. Throwing surfaces as a tool error to the model.

An outputSchema is required for HITL tools — it validates both the onToolCalled return value (when non-null) and the value delivered via function_call_output (whether transformed by onResponseReceived or passed through directly).

const approvePaymentTool = tool({
  name: 'approve_payment',
  description: 'Approve a payment, escalating large amounts to a human',
  inputSchema: z.object({
    amount: z.number(),
    recipient: z.string(),
  }),
  outputSchema: z.object({
    ok: z.boolean(),
    reviewedAt: z.number().optional(),
  }),
  onToolCalled: async (input) => {
    // Auto-approve small amounts
    if (input.amount < 100) {
      return { ok: true };
    }
    // Escalate to a human — pauses the loop
    return null;
  },
  onResponseReceived: async (raw) => {
    // Post-process the caller-supplied result before the model sees it
    return { ...(raw as object), reviewedAt: Date.now() };
  },
});

When onToolCalled returns null, the conversation state moves to status: 'awaiting_hitl' and the paused call surfaces via getToolCalls() / getPendingToolCalls(). Resume by calling callModel again with a function_call_output item for each paused call in the input.

HITL tools differ from requireApproval: approval gates pause before execution for a yes/no decision, while HITL tools let onToolCalled run arbitrary logic first and only pause when it returns null. Use HITL when the decision is data-driven (e.g., amount thresholds, risk scoring); use requireApproval when you always want explicit human consent. See Tool Approval & State.

Schema Definition

Input Schema

Define what parameters the tool accepts:

const inputSchema = z.object({
  // Required parameters
  query: z.string().describe('Search query'),

  // Optional with default
  limit: z.number().default(10).describe('Max results'),

  // Optional without default
  filter: z.string().optional().describe('Filter expression'),

  // Enum values
  sortBy: z.enum(['relevance', 'date', 'popularity']).default('relevance'),

  // Nested objects
  options: z.object({
    caseSensitive: z.boolean().default(false),
    wholeWord: z.boolean().default(false),
  }).optional(),

  // Arrays
  tags: z.array(z.string()).optional(),
});

Output Schema

Define the structure of results returned to the model:

const outputSchema = z.object({
  results: z.array(z.object({
    id: z.string(),
    title: z.string(),
    score: z.number(),
  })),
  metadata: z.object({
    totalCount: z.number(),
    searchTimeMs: z.number(),
  }),
});

Event Schema (Generator Tools)

Define progress/status events for generator tools:

const eventSchema = z.object({
  stage: z.enum(['initializing', 'processing', 'finalizing']),
  progress: z.number(),
  currentItem: z.string().optional(),
});

Type Inference

The SDK provides utilities to extract types from tools:

import type { InferToolInput, InferToolOutput, InferToolEvent } from '@openrouter/agent';

// Get the input type
type WeatherInput = InferToolInput<typeof weatherTool>;
// { location: string }

// Get the output type
type WeatherOutput = InferToolOutput<typeof weatherTool>;
// { temperature: number; conditions: string }

// Get event type (generator tools only)
type SearchEvent = InferToolEvent<typeof searchTool>;
// { progress: number; message: string }

Using Tools with callModel

Single Tool

const openrouter = new OpenRouter({ apiKey: process.env.OPENROUTER_API_KEY });

const result = openrouter.callModel({
  model: 'openai/gpt-5-nano',
  input: 'What is the weather in Tokyo?',
  tools: [weatherTool],
});

// Tools are automatically executed
const text = await result.getText();
// "The weather in Tokyo is 22°C and sunny."

Multiple Tools

const result = openrouter.callModel({
  model: 'openai/gpt-5-nano',
  input: 'Search for TypeScript tutorials and calculate 2+2',
  tools: [searchTool, calculatorTool],
});

Type-Safe Tool Calls with `as const`

Use as const for full type inference on tool calls:

const result = openrouter.callModel({
  model: 'openai/gpt-5-nano',
  input: 'What is the weather?',
  tools: [weatherTool, searchTool] as const,
  maxToolRounds: 0, // Get tool calls without executing
});

// Tool calls are typed as union of tool inputs
for await (const toolCall of result.getToolCallsStream()) {
  if (toolCall.name === 'get_weather') {
    // toolCall.arguments is typed as { location: string }
    console.log('Weather for:', toolCall.arguments.location);
  }
}

Execute Context

Tool execute functions receive a flat context object as their second argument. It merges TurnContext fields with a tools map and a setContext() method:

const contextAwareTool = tool({
  name: 'context_tool',
  inputSchema: z.object({ data: z.string() }),
  outputSchema: z.object({ result: z.string() }),
  execute: async (params, context) => {
    // TurnContext fields are available directly
    console.log('Turn:', context.numberOfTurns);
    console.log('History:', context.turnRequest?.input);
    console.log('Model:', context.turnRequest?.model);

    return {
      result: `Processed on turn ${context.numberOfTurns}`,
    };
  },
});

Context Properties

Property	Type	Description
`numberOfTurns`	`number`	Current turn number (1-indexed)
`turnRequest`	`OpenResponsesRequest \| undefined`	Current request object
`toolCall`	`OpenResponsesFunctionToolCall \| undefined`	The tool call being executed
`local`	`Readonly<TContext>`	This tool’s own context (read-only)
`setContext`	`(partial: Partial<TContext>) => void`	Mutate this tool’s context
`shared`	`Readonly<TShared>`	Shared context visible to all tools
`setSharedContext`	`(partial: Partial<TShared>) => void`	Mutate shared context

Tool Context

Tools can declare a contextSchema to receive typed, persistent context data from the caller. Context is keyed by tool name and persists across turns.

Declaring contextSchema

const weatherTool = tool({
  name: 'get_weather',
  description: 'Get weather for a location',
  inputSchema: z.object({
    location: z.string(),
  }),
  outputSchema: z.object({
    temperature: z.number(),
  }),
  // Declare what context this tool needs
  contextSchema: z.object({
    apiKey: z.string(),
    units: z.enum(['celsius', 'fahrenheit']),
  }),
  execute: async (params, context) => {
    // Access this tool's own context via local
    const { apiKey, units } = context.local;

    const weather = await fetchWeather(
      params.location,
      apiKey,
      units,
    );
    return { temperature: weather.temp };
  },
});

Providing Context in callModel

Pass context keyed by tool name:

const result = openrouter.callModel({
  model: 'openai/gpt-5-nano',
  input: 'What is the weather in Tokyo?',
  tools: [weatherTool, dbTool] as const,

  // Static context — keyed by tool name
  context: {
    get_weather: { apiKey: 'sk-...', units: 'celsius' },
    db_query: { connectionString: 'postgres://...' },
  },
});

Dynamic Context

Use an async function for one-time initialization that needs to fetch data:

const result = openrouter.callModel({
  model: 'openai/gpt-5-nano',
  input: 'What is the weather?',
  tools: [weatherTool] as const,

  // Resolved once at turn 0 to seed the store
  context: async () => ({
    get_weather: {
      apiKey: await fetchApiKey(),
      units: 'celsius',
    },
  }),
});

resolveContext runs once at turn 0 to seed the context store. For per-turn mutations, use setContext() inside your tool’s execute function.

Mutating Context with setContext

Tools can update their own context using setContext(). Changes persist across turns via the shared store and are visible immediately — context.local is a live getter that always reads the latest values:

const authTool = tool({
  name: 'auth',
  inputSchema: z.object({ action: z.string() }),
  contextSchema: z.object({
    token: z.string(),
    refreshCount: z.number(),
  }),
  execute: async (params, context) => {
    const { token } = context.local;

    if (isExpired(token)) {
      const newToken = await refreshToken(token);
      // Mutate own context — persists to next turn
      context.setContext({
        token: newToken,
        refreshCount:
          context.local.refreshCount + 1,
      });
    }

    return { success: true };
  },
});

Observing Context Changes

Use getContextUpdates() on ModelResult to observe context mutations in real time:

const result = openrouter.callModel({
  model: 'openai/gpt-5-nano',
  input: 'Authenticate and fetch data',
  tools: [authTool] as const,
  context: {
    auth: { token: 'initial', refreshCount: 0 },
  },
});

for await (const snapshot of result.getContextUpdates()) {
  console.log('Context changed:', snapshot);
  // { auth: { token: 'new-token', refreshCount: 1 } }
}

Shared Context

Use sharedSchema on tool() and sharedContextSchema on callModel to share typed state across tools:

const SharedContextSchema = z.object({
  _sessionId: z.string().optional(),
});

const execTool = tool({
  name: 'sandbox_exec',
  inputSchema: z.object({ command: z.string() }),
  sharedSchema: SharedContextSchema,
  execute: async (input, ctx) => {
    // Read shared state set by any tool
    const sid = ctx.shared._sessionId;
    const session = await connect(sid);
    // Write shared state for other tools
    ctx.setSharedContext({ _sessionId: session.id });
    return await session.exec(input.command);
  },
});

const result = openrouter.callModel({
  model: 'openai/gpt-5-nano',
  input: 'Run a command',
  tools: [execTool] as const,
  sharedContextSchema: SharedContextSchema,
  context: {
    shared: { _sessionId: 'existing-session' },
    sandbox_exec: {},
  },
});

context.local is scoped to one tool. context.shared is visible to all tools and persists across turns. Pass the same sharedSchema to each tool for typed access, and sharedContextSchema to callModel for runtime validation.

Tool Execution

callModel automatically executes tools and handles multi-turn conversations. When the model calls a tool, the SDK executes it, sends the result back, and continues until the model provides a final response.

Automatic Execution Flow

When you provide tools with execute functions:

import { OpenRouter, tool } from '@openrouter/agent';
import { z } from 'zod';

const weatherTool = tool({
  name: 'get_weather',
  inputSchema: z.object({ location: z.string() }),
  outputSchema: z.object({ temperature: z.number() }),
  execute: async ({ location }) => {
    return { temperature: await fetchTemperature(location) };
  },
});

const result = openrouter.callModel({
  model: 'openai/gpt-5-nano',
  input: 'What is the weather in Paris?',
  tools: [weatherTool],
});

// getText() waits for all tool execution to complete
const text = await result.getText();
// "The weather in Paris is 18°C."

Execution Sequence

Model receives prompt and generates tool call
SDK extracts tool call and validates arguments
Tool’s execute function runs
Result is formatted and sent back to model
Model generates final response (or more tool calls)
Process repeats until model is done

Controlling Execution Rounds

maxToolRounds (Number)

Limit the maximum number of tool execution rounds:

const result = openrouter.callModel({
  model: 'openai/gpt-5-nano',
  input: 'Research this topic thoroughly',
  tools: [searchTool, analyzeTool],
  maxToolRounds: 3, // Stop after 3 rounds of tool execution
});

Setting maxToolRounds: 0 disables automatic execution - you get raw tool calls.

maxToolRounds (Function)

Use a function for dynamic control:

const result = openrouter.callModel({
  model: 'openai/gpt-5-nano',
  input: 'Research and analyze',
  tools: [searchTool],
  maxToolRounds: (context) => {
    // Continue if under 5 turns
    return context.numberOfTurns < 5;
  },
});

The function receives TurnContext and returns true to continue or false to stop.

Accessing Tool Calls

getToolCalls()

Get all tool calls from the initial response (before auto-execution):

const result = openrouter.callModel({
  model: 'openai/gpt-5-nano',
  input: 'What is the weather in Tokyo and Paris?',
  tools: [weatherTool],
  maxToolRounds: 0, // Don't auto-execute
});

const toolCalls = await result.getToolCalls();

for (const call of toolCalls) {
  console.log(`Tool: ${call.name}`);
  console.log(`ID: ${call.id}`);
  console.log(`Arguments:`, call.arguments);
}

getToolCallsStream()

Stream tool calls as they complete:

const result = openrouter.callModel({
  model: 'openai/gpt-5-nano',
  input: 'Check weather in multiple cities',
  tools: [weatherTool],
  maxToolRounds: 0,
});

for await (const toolCall of result.getToolCallsStream()) {
  console.log(`Received tool call: ${toolCall.name}`);

  // Process each tool call as it arrives
  const weatherResult = await processWeatherRequest(toolCall.arguments);
  console.log('Result:', weatherResult);
}

Tool Stream Events

getToolStream()

Stream both argument deltas and preliminary results:

const searchTool = tool({
  name: 'search',
  inputSchema: z.object({ query: z.string() }),
  eventSchema: z.object({ progress: z.number(), status: z.string() }),
  outputSchema: z.object({ results: z.array(z.string()) }),
  execute: async function* ({ query }) {
    yield { progress: 25, status: 'Searching...' };
    yield { progress: 50, status: 'Processing...' };
    yield { progress: 75, status: 'Ranking...' };
    yield { progress: 100, status: 'Complete' };
    return { results: ['result1', 'result2'] };
  },
});

const result = openrouter.callModel({
  model: 'openai/gpt-5-nano',
  input: 'Search for TypeScript tutorials',
  tools: [searchTool],
});

for await (const event of result.getToolStream()) {
  switch (event.type) {
    case 'delta':
      // Raw argument delta from the model
      process.stdout.write(event.content);
      break;
    case 'preliminary_result':
      // Progress from generator tool
      console.log(`Progress: ${event.result.progress}% - ${event.result.status}`);
      break;
  }
}

Event Types

Type	Description
`delta`	Raw tool call argument chunks from model
`preliminary_result`	Progress events from generator tools (intermediate yields)

Tool Result Events

When using getFullResponsesStream(), you can also receive tool.result events that fire when a tool execution completes:

for await (const event of result.getFullResponsesStream()) {
  switch (event.type) {
    case 'tool.preliminary_result':
      // Intermediate progress from generator tools
      console.log(`Progress (${event.toolCallId}):`, event.result);
      break;
    case 'tool.result':
      // Final result when tool execution completes
      console.log(`Tool ${event.toolCallId} completed`);
      console.log('Result:', event.result);
      // Access any preliminary results that were emitted during execution
      if (event.preliminaryResults) {
        console.log('All progress events:', event.preliminaryResults);
      }
      break;
  }
}

ToolResultEvent Type

type ToolResultEvent<TResult = unknown, TPreliminaryResults = unknown> = {
  type: 'tool.result';
  toolCallId: string;
  result: TResult;
  timestamp: number;
  preliminaryResults?: TPreliminaryResults[];
};

The tool.result event provides the final output from tool execution along with all intermediate preliminaryResults that were yielded during execution (for generator tools). This is useful when you need both real-time progress updates and a summary of all progress at completion.

Parallel Tool Execution

When the model calls multiple tools, they execute in parallel:

const result = openrouter.callModel({
  model: 'openai/gpt-5-nano',
  input: 'Get weather in Paris, Tokyo, and New York simultaneously',
  tools: [weatherTool],
});

// All three weather calls execute in parallel
const text = await result.getText();

Manual Tool Handling

For tools without execute functions:

const confirmTool = tool({
  name: 'send_email',
  description: 'Send an email (requires confirmation)',
  inputSchema: z.object({
    to: z.string().email(),
    subject: z.string(),
    body: z.string(),
  }),
  execute: false, // Manual handling
});

const result = openrouter.callModel({
  model: 'openai/gpt-5-nano',
  input: 'Send an email to alice@example.com',
  tools: [confirmTool],
  maxToolRounds: 0,
});

const toolCalls = await result.getToolCalls();

for (const call of toolCalls) {
  if (call.name === 'send_email') {
    // Show confirmation UI
    const confirmed = await showConfirmDialog(call.arguments);

    if (confirmed) {
      await sendEmail(call.arguments);
    }
  }
}

Execution Results

Access execution metadata through getResponse():

const result = openrouter.callModel({
  model: 'openai/gpt-5-nano',
  input: 'What is 2+2 and the weather in Paris?',
  tools: [calculatorTool, weatherTool],
});

const response = await result.getResponse();

// Response includes all execution rounds
console.log('Final output:', response.output);
console.log('Usage:', response.usage);

Error Handling

Tool Execution Errors

Errors in execute functions are caught and sent back to the model:

const riskyTool = tool({
  name: 'risky_operation',
  inputSchema: z.object({ input: z.string() }),
  outputSchema: z.object({ result: z.string() }),
  execute: async (params) => {
    if (params.input === 'fail') {
      throw new Error('Operation failed: invalid input');
    }
    return { result: 'success' };
  },
});

const result = openrouter.callModel({
  model: 'openai/gpt-5-nano',
  input: 'Try the risky operation with "fail"',
  tools: [riskyTool],
});

// Model receives error message and can respond appropriately
const text = await result.getText();
// "I tried the operation but it failed with: Operation failed: invalid input"

Validation Errors

Invalid tool arguments are caught before execution:

const strictTool = tool({
  name: 'strict',
  inputSchema: z.object({
    email: z.string().email(),
    age: z.number().min(0).max(150),
  }),
  execute: async (params) => {
    // Only runs with valid input
    return { valid: true };
  },
});

Graceful Error Handling

Handle errors gracefully in execute functions:

const robustTool = tool({
  name: 'fetch_data',
  inputSchema: z.object({ url: z.string().url() }),
  outputSchema: z.object({
    data: z.unknown().optional(),
    error: z.string().optional(),
  }),
  execute: async (params) => {
    try {
      const response = await fetch(params.url);
      if (!response.ok) {
        return { error: `HTTP ${response.status}: ${response.statusText}` };
      }
      return { data: await response.json() };
    } catch (error) {
      return { error: `Failed to fetch: ${error.message}` };
    }
  },
});

Best Practices

Descriptive Names and Descriptions

// Good: Clear name and description
const tool1 = tool({
  name: 'search_knowledge_base',
  description: 'Search the company knowledge base for documents, FAQs, and policies. Returns relevant articles with snippets.',
  // ...
});

// Avoid: Vague or generic
const tool2 = tool({
  name: 'search',
  description: 'Searches stuff',
  // ...
});

Schema Descriptions

Add .describe() to help the model understand parameters:

const inputSchema = z.object({
  query: z.string().describe('Natural language search query'),
  maxResults: z.number()
    .min(1)
    .max(100)
    .default(10)
    .describe('Maximum number of results to return (1-100)'),
  dateRange: z.enum(['day', 'week', 'month', 'year', 'all'])
    .default('all')
    .describe('Filter results by time period'),
});

Idempotent Tools

Design tools to be safely re-executable:

const createUserTool = tool({
  name: 'create_user',
  inputSchema: z.object({
    email: z.string().email(),
    name: z.string(),
  }),
  execute: async (params) => {
    // Check if user exists first
    const existing = await findUserByEmail(params.email);
    if (existing) {
      return { userId: existing.id, created: false };
    }

    const user = await createUser(params);
    return { userId: user.id, created: true };
  },
});

Timeout Handling

Wrap long-running operations:

const longRunningTool = tool({
  name: 'process_data',
  inputSchema: z.object({ dataId: z.string() }),
  execute: async (params) => {
    const timeoutMs = 30000;

    const result = await Promise.race([
      processData(params.dataId),
      new Promise((_, reject) =>
        setTimeout(() => reject(new Error('Operation timed out')), timeoutMs)
      ),
    ]);

    return result;
  },
});

Next Steps

Tool Approval & State - Human-in-the-loop approval and conversation persistence
nextTurnParams - Tool-driven context injection
Stop Conditions - Advanced execution control
Examples - Complete tool implementations

​The tool() Helper

​Tool Types

​Regular Tools

​Generator Tools

​Manual Tools

​Human-in-the-Loop (HITL) Tools

​Schema Definition

​Input Schema

​Output Schema

​Event Schema (Generator Tools)

​Type Inference

​Using Tools with callModel

​Single Tool

​Multiple Tools

​Type-Safe Tool Calls with as const

​Execute Context

​Context Properties

​Tool Context

​Declaring contextSchema

​Providing Context in callModel

​Dynamic Context

​Mutating Context with setContext

​Observing Context Changes

​Shared Context

​Tool Execution

​Automatic Execution Flow

​Execution Sequence

​Controlling Execution Rounds

​maxToolRounds (Number)

​maxToolRounds (Function)

​Accessing Tool Calls

​getToolCalls()

​getToolCallsStream()

​Tool Stream Events

​getToolStream()

​Event Types

​Tool Result Events

​ToolResultEvent Type

​Parallel Tool Execution

​Manual Tool Handling

​Execution Results

​Error Handling

​Tool Execution Errors

​Validation Errors

​Graceful Error Handling

​Best Practices

​Descriptive Names and Descriptions

​Schema Descriptions

​Idempotent Tools

​Timeout Handling

​Next Steps

The tool() Helper

Tool Types

Regular Tools

Generator Tools

Manual Tools

Human-in-the-Loop (HITL) Tools

Schema Definition

Input Schema

Output Schema

Event Schema (Generator Tools)

Type Inference

Using Tools with callModel

Single Tool

Multiple Tools

Type-Safe Tool Calls with `as const`

Execute Context

Context Properties

Tool Context

Declaring contextSchema

Providing Context in callModel

Dynamic Context

Mutating Context with setContext

Observing Context Changes

Shared Context

Tool Execution

Automatic Execution Flow

Execution Sequence

Controlling Execution Rounds

maxToolRounds (Number)

maxToolRounds (Function)

Accessing Tool Calls

getToolCalls()

getToolCallsStream()

Tool Stream Events

getToolStream()

Event Types

Tool Result Events

ToolResultEvent Type

Parallel Tool Execution

Manual Tool Handling

Execution Results

Error Handling

Tool Execution Errors

Validation Errors

Graceful Error Handling

Best Practices

Descriptive Names and Descriptions

Schema Descriptions

Idempotent Tools

Timeout Handling

Next Steps