AI

experimental_createMCPClient()

Creates a lightweight Model Context Protocol (MCP) client that connects to an MCP server. The client's primary purpose is tool conversion between MCP tools and AI SDK tools.

It currently does not support accepting notifications from an MCP server, and custom configuration of the client.

This feature is experimental and may change or be removed in the future.

Import 

import { experimental_createMCPClient } from "ai"

API Signature

Parameters

config:

MCPClientConfig
Configuration for the MCP client.
MCPClientConfig

transport:

TransportConfig = MCPTransport | McpSSEServerConfig
Configuration for the message transport layer.
MCPTransport

start:

() => Promise<void>
A method that starts the transport

send:

(message: JSONRPCMessage) => Promise<void>
A method that sends a message through the transport

close:

() => Promise<void>
A method that closes the transport

onclose:

() => void
A method that is called when the transport is closed

onerror:

(error: Error) => void
A method that is called when the transport encounters an error

onmessage:

(message: JSONRPCMessage) => void
A method that is called when the transport receives a message
McpSSEServerConfig

type:

'sse'
Use Server-Sent Events for communication

url:

string
URL of the MCP server

headers?:

Record<string, string>
Additional HTTP headers to be sent with requests.

name?:

string
Client name. Defaults to "ai-sdk-mcp-client"

onUncaughtError?:

(error: unknown) => void
Handler for uncaught errors

Returns

Returns a Promise that resolves to an MCPClient with the following methods:

tools:

async (options?: { schemas?: TOOL_SCHEMAS }) => Promise<McpToolSet<TOOL_SCHEMAS>>
Gets the tools available from the MCP server.
options

schemas?:

TOOL_SCHEMAS
Schema definitions for compile-time type checking. When not provided, schemas are inferred from the server.

close:

async () => void
Closes the connection to the MCP server and cleans up resources.

Example 

import { experimental_createMCPClient, generateText } from 'ai';
import { openai } from '@ai-sdk/openai';


try {
  const client = await experimental_createMCPClient({
    transport: {
      type: 'stdio',
      command: 'node server.js',
    },
  });


  const tools = await client.tools();


  const response = await generateText({
    model: openai('gpt-4o-mini'),
    tools,
    messages: [{ role: 'user', content: 'Query the data' }],
  });


  console.log(response);
} finally {
  await client.close();
}

Error Handling

The client throws MCPClientError for:

  • Client initialization failures
  • Protocol version mismatches
  • Missing server capabilities
  • Connection failures

For tool execution, errors are propagated as CallToolError errors.

For unknown errors, the client exposes an onUncaughtError callback that can be used to manually log or handle errors that are not covered by known error types.