@ai-sdk/mcp

AI SDK - Model Context Protocol Client

The Model Context Protocol (MCP) client for the AI SDK lets you connect to MCP servers and use their tools with AI SDK functions like generateText and streamText.

Setup

The MCP client is available in the @ai-sdk/mcp module. You can install it with

npm i @ai-sdk/mcp ai zod

Skill for Coding Agents

If you use coding agents such as Claude Code or Cursor, we highly recommend adding the AI SDK skill to your repository:

npx skills add vercel/ai

Usage

Create an MCP client with createMCPClient(), fetch the server tools with mcpClient.tools(), and pass them to an AI SDK call:

import { createMCPClient } from '@ai-sdk/mcp';
import { generateText, isStepCount } from 'ai';

const mcpClient = await createMCPClient({
  transport: {
    type: 'http',
    url: 'https://your-server.com/mcp',
    headers: {
      Authorization: `Bearer ${process.env.MCP_API_KEY}`,
    },
  },
});

try {
  const tools = await mcpClient.tools();

  const { text } = await generateText({
    model: 'openai/gpt-5.4',
    tools,
    stopWhen: isStepCount(10),
    prompt: 'Use the available tools to answer the user question.',
  });

  console.log(text);
} finally {
  await mcpClient.close();
}

The client converts MCP tool definitions into AI SDK tools, so model calls can use them through the standard tools option.

For streaming responses, close the MCP client when the stream finishes:

import { createMCPClient } from '@ai-sdk/mcp';
import { streamText } from 'ai';

const mcpClient = await createMCPClient({
  transport: {
    type: 'http',
    url: 'https://your-server.com/mcp',
  },
});

const result = streamText({
  model: 'openai/gpt-5.4',
  tools: await mcpClient.tools(),
  prompt: 'Use the available tools to answer the user question.',
  onEnd: async () => {
    await mcpClient.close();
  },
});

for await (const textPart of result.textStream) {
  process.stdout.write(textPart);
}

Transports

HTTP is recommended for production deployments:

import { createMCPClient } from '@ai-sdk/mcp';

const savedSession = await loadMcpSession();
let currentSessionId = savedSession?.sessionId;

const mcpClient = await createMCPClient({
  transport: {
    type: 'http',
    url: 'https://your-server.com/mcp',
    initialSessionId: savedSession?.sessionId,
    initialProtocolVersion: savedSession?.initializeResult.protocolVersion,
    terminateSessionOnClose: false,
    onSessionIdChange: sessionId => {
      currentSessionId = sessionId;
    },
    onSessionExpired: sessionId => {
      if (currentSessionId === sessionId) {
        currentSessionId = undefined;
        void clearMcpSession();
      }
    },
  },
  initialInitializeResult: savedSession?.initializeResult,
});

if (currentSessionId) {
  await saveMcpSession({
    sessionId: currentSessionId,
    initializeResult: mcpClient.initializeResult,
  });
}

SSE is also supported for MCP servers that use Server-Sent Events:

const mcpClient = await createMCPClient({
  transport: {
    type: 'sse',
    url: 'https://your-server.com/sse',
  },
});

For local MCP servers, you can use stdio transport from the @ai-sdk/mcp/mcp-stdio subpath:

import { createMCPClient } from '@ai-sdk/mcp';
import { Experimental_StdioMCPTransport } from '@ai-sdk/mcp/mcp-stdio';

const mcpClient = await createMCPClient({
  transport: new Experimental_StdioMCPTransport({
    command: 'node',
    args: ['server.js'],
  }),
});

Documentation

Please check out the AI SDK MCP documentation for more information.