DocsAPI ReferenceSDKsCookbookChangelog

For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://modelgates.ai/docs/_mcp/server.

Tools

The tool() Helper

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

typescript
import { ModelGates, tool } from '@modelgates/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:

typescript
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:

typescript
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:

typescript
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).

typescript
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:

typescript
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:

typescript
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:

typescript
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:

typescript
import type { InferToolInput, InferToolOutput, InferToolEvent } from '@modelgates/agent'; // Get the input typetype WeatherInput = InferToolInput<typeof weatherTool>;// { location: string } // Get the output typetype 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

typescript
const modelgates = new ModelGates({ apiKey: process.env.MODELGATES_API_KEY }); const result = modelgates.callModel({  model: 'openai/gpt-5-nano',  input: 'What is the weather in Tokyo?',  tools: [weatherTool],}); // Tools are automatically executedconst text = await result.getText();// "The weather in Tokyo is 22°C and sunny."

Multiple Tools

typescript
const result = modelgates.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:

typescript
const result = modelgates.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 inputsfor 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:

typescript
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

PropertyTypeDescription
numberOfTurnsnumberCurrent turn number (1-indexed)
turnRequestOpenResponsesRequest | undefinedCurrent request object
toolCallOpenResponsesFunctionToolCall | undefinedThe tool call being executed
localReadonly<TContext>This tool's own context (read-only)
setContext(partial: Partial<TContext>) => voidMutate this tool's context
sharedReadonly<TShared>Shared context visible to all tools
setSharedContext(partial: Partial<TShared>) => voidMutate 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

typescript
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:

typescript
const result = modelgates.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:

typescript
const result = modelgates.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:

typescript
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:

typescript
const result = modelgates.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:

typescript
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 = modelgates.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:

typescript
import { ModelGates, tool } from '@modelgates/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 = modelgates.callModel({  model: 'openai/gpt-5-nano',  input: 'What is the weather in Paris?',  tools: [weatherTool],}); // getText() waits for all tool execution to completeconst text = await result.getText();// "The weather in Paris is 18°C."

Execution Sequence

  1. Model receives prompt and generates tool call
  2. SDK extracts tool call and validates arguments
  3. Tool's execute function runs
  4. Result is formatted and sent back to model
  5. Model generates final response (or more tool calls)
  6. Process repeats until model is done

Controlling Execution Rounds

maxToolRounds (Number)

Limit the maximum number of tool execution rounds:

typescript
const result = modelgates.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:

typescript
const result = modelgates.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):

typescript
const result = modelgates.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: $`);  console.log(`ID: $`);  console.log(`Arguments:`, call.arguments);}

getToolCallsStream()

Stream tool calls as they complete:

typescript
const result = modelgates.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: $`);   // 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:

typescript
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 = modelgates.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

TypeDescription
deltaRaw tool call argument chunks from model
preliminary_resultProgress 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:

typescript
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

typescript
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:

typescript
const result = modelgates.callModel({  model: 'openai/gpt-5-nano',  input: 'Get weather in Paris, Tokyo, and New York simultaneously',  tools: [weatherTool],}); // All three weather calls execute in parallelconst text = await result.getText();

Manual Tool Handling

For tools without execute functions:

typescript
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 = modelgates.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():

typescript
const result = modelgates.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 roundsconsole.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:

typescript
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 = modelgates.callModel({  model: 'openai/gpt-5-nano',  input: 'Try the risky operation with "fail"',  tools: [riskyTool],}); // Model receives error message and can respond appropriatelyconst 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:

typescript
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:

typescript
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

typescript
// Good: Clear name and descriptionconst 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 genericconst tool2 = tool({  name: 'search',  description: 'Searches stuff',  // ...});

Schema Descriptions

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

typescript
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:

typescript
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:

typescript
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