AI

experimental_createMCPClient()

Creates a lightweight Model Context Protocol (MCP) client that connects to an MCP server. The client provides:

  • Tools: Automatic conversion between MCP tools and AI SDK tools
  • Resources: Methods to list, read, and discover resource templates from MCP servers

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-sdk/mcp"

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
MCPTransportConfig

type:

'sse' | 'http
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.

authProvider?:

OAuthClientProvider
Optional OAuth provider for authorization to access protected remote MCP servers.

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.

listResources:

async (options?: { params?: PaginatedRequest['params']; options?: RequestOptions; }) => Promise<ListResourcesResult>
Lists all available resources from the MCP server.
options

params?:

PaginatedRequest['params']
Optional pagination parameters including cursor.

options?:

RequestOptions
Optional request options including signal and timeout.

readResource:

async (args: { uri: string; options?: RequestOptions; }) => Promise<ReadResourceResult>
Reads the contents of a specific resource by URI.
args

uri:

string
The URI of the resource to read.

options?:

RequestOptions
Optional request options including signal and timeout.

listResourceTemplates:

async (options?: { options?: RequestOptions; }) => Promise<ListResourceTemplatesResult>
Lists all available resource templates from the MCP server.
options

options?:

RequestOptions
Optional request options including signal and timeout.

close:

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

Example 

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


let client;


try {
  client = await experimental_createMCPClient({
    transport: new Experimental_StdioMCPTransport({
      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);
} catch (error) {
  console.error('Error:', error);
} finally {
  // ensure the client is closed even if an error occurs
  if (client) {
    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.