@tanstack/ai/skills/ai-core/tool-calling/SKILL.md

Core TanStack AI library - Open source AI SDK

---
name: ai-core/tool-calling
description: >
  Isomorphic tool system: toolDefinition() with Zod schemas,
  .server() and .client() implementations, passing tools to both
  chat() on server and useChat/clientTools on client, tool approval
  flows with needsApproval and addToolApprovalResponse(), lazy tool
  discovery with lazy:true, rendering ToolCallPart and ToolResultPart
  in UI.
type: sub-skill
library: tanstack-ai
library_version: '0.10.0'
sources:
  - 'TanStack/ai:docs/tools/tools.md'
  - 'TanStack/ai:docs/tools/server-tools.md'
  - 'TanStack/ai:docs/tools/client-tools.md'
  - 'TanStack/ai:docs/tools/tool-approval.md'
  - 'TanStack/ai:docs/tools/lazy-tool-discovery.md'
---

# Tool Calling

This skill builds on ai-core. Read it first for critical rules.

## Setup

Complete end-to-end example: shared definition, server tool, client tool, server route, React client.

```typescript
// tools/definitions.ts
import { toolDefinition } from '@tanstack/ai'
import { z } from 'zod'

export const getProductsDef = toolDefinition({
  name: 'get_products',
  description: 'Search for products in the catalog',
  inputSchema: z.object({
    query: z.string().meta({ description: 'Search keyword' }),
    limit: z.number().optional().meta({ description: 'Max results' }),
  }),
  outputSchema: z.object({
    products: z.array(
      z.object({ id: z.string(), name: z.string(), price: z.number() }),
    ),
  }),
})

export const updateCartUIDef = toolDefinition({
  name: 'update_cart_ui',
  description: 'Update the shopping cart UI with item count',
  inputSchema: z.object({ itemCount: z.number(), message: z.string() }),
  outputSchema: z.object({ displayed: z.boolean() }),
})
```

```typescript
// tools/server.ts
import { getProductsDef } from './definitions'

export const getProducts = getProductsDef.server(async ({ query, limit }) => {
  const results = await db.products.search(query, { limit: limit ?? 10 })
  return {
    products: results.map((p) => ({ id: p.id, name: p.name, price: p.price })),
  }
})
```

```typescript
// api/chat/route.ts
import { chat, toServerSentEventsResponse } from '@tanstack/ai'
import { openaiText } from '@tanstack/ai-openai'
import { getProducts } from '@/tools/server'
import { updateCartUIDef } from '@/tools/definitions'

export async function POST(request: Request) {
  const { messages } = await request.json()
  const stream = chat({
    adapter: openaiText('gpt-4o'),
    messages,
    tools: [getProducts, updateCartUIDef], // server tool + client definition
  })
  return toServerSentEventsResponse(stream)
}
```

```typescript
// app/chat.tsx
import {
  useChat,
  fetchServerSentEvents,
  clientTools,
  createChatClientOptions,
  type InferChatMessages,
} from "@tanstack/ai-react";
import { updateCartUIDef } from "@/tools/definitions";
import { useState } from "react";

function ChatPage() {
  const [cartCount, setCartCount] = useState(0);

  const updateCartUI = updateCartUIDef.client((input) => {
    setCartCount(input.itemCount);
    return { displayed: true };
  });

  const tools = clientTools(updateCartUI);
  const chatOptions = createChatClientOptions({
    connection: fetchServerSentEvents("/api/chat"),
    tools,
  });
  type Messages = InferChatMessages<typeof chatOptions>;

  const { messages, sendMessage } = useChat(chatOptions);

  return (
    <div>
      <span>Cart: {cartCount}</span>
      {(messages as Messages).map((msg) => (
        <div key={msg.id}>
          {msg.parts.map((part) => {
            if (part.type === "text") return <p>{part.content}</p>;
            if (part.type === "tool-call") {
              return <div key={part.id}>Tool: {part.name} ({part.state})</div>;
            }
            return null;
          })}
        </div>
      ))}
    </div>
  );
}
```

## Core Patterns

### Pattern 1: Server-Only Tool

Define with `toolDefinition()`, implement with `.server()`, pass to `chat({ tools })`.
The server executes it automatically. The client never runs code for this tool.

```typescript
import { toolDefinition } from '@tanstack/ai'
import { z } from 'zod'

const getUserDataDef = toolDefinition({
  name: 'get_user_data',
  description: 'Look up user by ID',
  inputSchema: z.object({
    userId: z.string().meta({ description: "The user's ID" }),
  }),
  outputSchema: z.object({ name: z.string(), email: z.string() }),
})

const getUserData = getUserDataDef.server(async ({ userId }) => {
  const user = await db.users.findUnique({ where: { id: userId } })
  return { name: user.name, email: user.email }
})

// In your route handler:
const stream = chat({
  adapter: openaiText('gpt-4o'),
  messages,
  tools: [getUserData],
})
```

### Pattern 2: Client-Only Tool

Pass the bare definition (no `.server()`) to `chat({ tools })` so the LLM knows
about it. Pass the `.client()` implementation to `useChat` via `clientTools()`.

```typescript
import { toolDefinition } from '@tanstack/ai'
import { z } from 'zod'

export const showNotificationDef = toolDefinition({
  name: 'show_notification',
  description: 'Display a toast notification to the user',
  inputSchema: z.object({
    message: z.string(),
    type: z.enum(['success', 'error', 'info']),
  }),
  outputSchema: z.object({ shown: z.boolean() }),
})
```

Server -- pass definition only (no execute function):

```typescript
const stream = chat({
  adapter: openaiText('gpt-4o'),
  messages,
  tools: [showNotificationDef],
})
```

Client -- pass `.client()` implementation:

```typescript
import {
  useChat,
  fetchServerSentEvents,
  clientTools,
  createChatClientOptions,
} from "@tanstack/ai-react";
import { showNotificationDef } from "@/tools/definitions";
import { useState } from "react";

function ChatPage() {
  const [toast, setToast] = useState<string | null>(null);

  const showNotification = showNotificationDef.client((input) => {
    setToast(input.message);
    setTimeout(() => setToast(null), 3000);
    return { shown: true };
  });

  const { messages, sendMessage } = useChat(
    createChatClientOptions({
      connection: fetchServerSentEvents("/api/chat"),
      tools: clientTools(showNotification),
    })
  );

  return (
    <div>
      {toast && <div className="toast">{toast}</div>}
      {messages.map((msg) => (
        <div key={msg.id}>
          {msg.parts.map((part) =>
            part.type === "text" ? <p>{part.content}</p> : null
          )}
        </div>
      ))}
    </div>
  );
}
```

### Pattern 3: Tool with Approval Flow

Set `needsApproval: true` in the definition. Execution pauses until the client
calls `addToolApprovalResponse()`. The part has `state: "approval-requested"`
and an `approval` object with an `id`.

```typescript
import { toolDefinition } from '@tanstack/ai'
import { z } from 'zod'

export const sendEmailDef = toolDefinition({
  name: 'send_email',
  description: 'Send an email to a recipient',
  inputSchema: z.object({
    to: z.string().email(),
    subject: z.string(),
    body: z.string(),
  }),
  outputSchema: z.object({ success: z.boolean(), messageId: z.string() }),
  needsApproval: true,
})

export const sendEmail = sendEmailDef.server(async ({ to, subject, body }) => {
  const result = await emailService.send({ to, subject, body })
  return { success: true, messageId: result.id }
})
```

Client -- render approval UI and respond:

```typescript
import { useChat, fetchServerSentEvents } from "@tanstack/ai-react";

function ChatPage() {
  const { messages, addToolApprovalResponse } = useChat({
    connection: fetchServerSentEvents("/api/chat"),
  });

  return (
    <div>
      {messages.map((msg) => (
        <div key={msg.id}>
          {msg.parts.map((part) => {
            if (part.type === "text") return <p>{part.content}</p>;
            if (
              part.type === "tool-call" &&
              part.state === "approval-requested" &&
              part.approval
            ) {
              return (
                <div key={part.id}>
                  <p>Approve "{part.name}"?</p>
                  <pre>{part.arguments}</pre>
                  <button
                    onClick={() =>
                      addToolApprovalResponse({
                        id: part.approval!.id,
                        approved: true,
                      })
                    }
                  >
                    Approve
                  </button>
                  <button
                    onClick={() =>
                      addToolApprovalResponse({
                        id: part.approval!.id,
                        approved: false,
                      })
                    }
                  >
                    Deny
                  </button>
                </div>
              );
            }
            return null;
          })}
        </div>
      ))}
    </div>
  );
}
```

### Pattern 4: Lazy Tool Discovery

Set `lazy: true` on rarely-needed tools. The LLM sees their names via a synthetic
`__lazy__tool__discovery__` tool and discovers schemas on demand. Saves tokens.

```typescript
import {
  toolDefinition,
  chat,
  toServerSentEventsResponse,
  maxIterations,
} from '@tanstack/ai'
import { openaiText } from '@tanstack/ai-openai'
import { z } from 'zod'

const getProductsDef = toolDefinition({
  name: 'getProducts',
  description: 'List all products',
  inputSchema: z.object({}),
  outputSchema: z.array(
    z.object({ id: z.number(), name: z.string(), price: z.number() }),
  ),
})
const getProducts = getProductsDef.server(async () => db.products.findMany())

const compareProductsDef = toolDefinition({
  name: 'compareProducts',
  description: 'Compare two or more products side by side',
  inputSchema: z.object({ productIds: z.array(z.number()).min(2) }),
  lazy: true, // not sent to LLM upfront
})
const compareProducts = compareProductsDef.server(async ({ productIds }) => {
  return db.products.findMany({ where: { id: { in: productIds } } })
})

export async function POST(request: Request) {
  const { messages } = await request.json()
  const stream = chat({
    adapter: openaiText('gpt-4o'),
    messages,
    tools: [getProducts, compareProducts],
    agentLoopStrategy: maxIterations(20),
  })
  return toServerSentEventsResponse(stream)
}
```

The LLM sees `getProducts` and `__lazy__tool__discovery__` upfront.
To compare, it first calls `__lazy__tool__discovery__({ toolNames: ["compareProducts"] })`,
gets the full schema, then calls `compareProducts` directly.
Once discovered, a tool stays available for the conversation.
When all lazy tools are discovered, the discovery tool is removed automatically.

## Common Mistakes

### a. HIGH: Not passing tool definitions to both server and client

Server tools need `chat({ tools })`. Client tools need their definition in
`chat({ tools })` AND their `.client()` in `useChat({ tools: clientTools(...) })`.

Wrong -- tool only on server, client cannot execute:

```typescript
chat({ adapter, messages, tools: [myToolDef] })
useChat({ connection: fetchServerSentEvents('/api/chat') }) // no tools
```

Wrong -- tool only on client, LLM does not know about it:

```typescript
chat({ adapter, messages }); // no tools
useChat({ ..., tools: clientTools(myToolDef.client(() => result)) });
```

Correct:

```typescript
chat({ adapter, messages, tools: [myToolDef] });
useChat({ ..., tools: clientTools(myToolDef.client((input) => ({ success: true }))) });
```

Source: docs/tools/tools.md

## Cross-References

- See also: ai-core/chat-experience/SKILL.md -- Tools are used within chat
- See also: `@tanstack/ai-code-mode` package skills -- Code Mode is an alternative to tools for complex multi-step operations