MCP to LangChain Tools Conversion Library / TypeScript   

A simple, lightweight library to use

Model Context Protocol (MCP)

server tools from LangChain.

Project Status

This library currently supports MCP Protocol version 2025-03-26.

It does not support the MCP 2026-07-28 specification.

For new projects, we recommend using

LangChain's official LangChain.js MCP Adapters library,

which provides comprehensive MCP integration for LangChain.

This project is now in maintenance / dormant mode. Existing users may continue

using it with MCP servers compatible with the 2025-03-26 specification, but new

MCP protocol features will not be actively implemented here.

Introduction

A simple, lightweight library to use

Model Context Protocol (MCP)

server tools from LangChain.

Its simplicity and extra features, such as

tools schema adjustments for LLM compatibility

and tools invocation logging,

make it a useful basis for your experiments and customizations.

However, it only supports text results of tool calls and does not support MCP features other than Tools.

LangChain's **official LangChain.js MCP Adapters** library,

which supports comprehensive integration with LangChain, has been released.

For new projects, it is the recommended MCP integration path for LangChain.

Prerequisites

• Node.js 18+
• [optional] npm 7+ (`npx`)

to run Node.js package-based local MCP servers

• [optional] `uv` (`uvx`)

installed to run Python-based local (stdio) MCP servers

Installation

npm i @h1deya/langchain-mcp-tools

Quick Start

`convertMcpToLangchainTools()` utility function accepts MCP server configurations

that follow the same structure as

Claude for Desktop,

but only the contents of the `mcpServers` property,

and is expressed as a JS Object, e.g.:

const mcpServers: McpServersConfig = {
  filesystem: {
    command: 'npx',
    args: ['-y', '@modelcontextprotocol/server-filesystem', '.'],
  },
  fetch: {
    command: 'uvx',
    args: ['mcp-server-fetch'],
  },
  'brave-search': {
    command: 'npx',
    args: ['-y', '@modelcontextprotocol/server-brave-search'],
    env: { BRAVE_API_KEY: process.env.BRAVE_API_KEY || '' },
  },
  github: {
    type: 'http',
    url: 'https://api.githubcopilot.com/mcp/',
    headers: {
      Authorization: `Bearer ${process.env.GITHUB_PERSONAL_ACCESS_TOKEN}`,
    },
  },
  notion: {
    // For MCP servers that require OAuth, consider using "mcp-remote"
    command: 'npx',
    args: ['-y', 'mcp-remote', 'https://mcp.notion.com/mcp'],
  },
}

const { tools, cleanup } = await convertMcpToLangchainTools(mcpServers, {
  // Perform provider-specific JSON schema transformations to prevent schema compatibility issues
  llmProvider: 'google_gemini',
  // llmProvider: "openai"
  // llmProvider: "anthropic"  // no transformations
  // llmProvider: "xai"  // no transformations
  // llmProvider: "none"  // for others (default)
})

This utility function initializes all specified MCP servers in parallel,

and returns LangChain Tools

([`tools: StructuredTool[]`](https://api.js.langchain.com/classes/_langchain_core.tools.StructuredTool.html))

by gathering available MCP tools from the servers,

and by wrapping them into LangChain tools.

When `llmProvider` option is specified, it performs LLM provider-specific schema transformations

for MCP tools to prevent schema compatibility issues.

Set this option when you enconter schema related warnings/errors while execution.

See below for details.

It also returns an async callback function (`cleanup: McpServerCleanupFn`)

to be invoked to close all MCP server sessions when finished.

The returned tools can be used with LangChain, e.g.:

// import { ChatGoogleGenerativeAI } from "@langchain/google-genai";
const model = new ChatGoogleGenerativeAI({ model: 'gemini-2.5-flash' })

// import { createAgent } from "langchain";
const agent = createAgent({
  model,
  tools,
})

The returned `cleanup` function properly handles resource cleanup:

• Closes all MCP server connections concurrently and logs any cleanup failures
• Continues cleanup of remaining servers even if some fail
• Should always be called when done using the tools

It is typically invoked in a finally block:

let mcpCleanup: McpServerCleanupFn | undefined

try {
  const { tools, cleanup } = await convertMcpToLangchainTools(mcpServers)
  mcpCleanup = cleanup
  // Use tools with your LLM
} finally {
  // Always cleanup, even if errors occur
  await mcpCleanup?.()
}

A simple working usage example can be found

in this example in the langchain-mcp-tools-ts-usage repo

For hands-on experimentation with MCP server integration,

try this MCP Client CLI tool built with this library

A Python equivalent of this utility is available

here

API Reference

Can be found here

Change Log

Can be found here

Working Usage Example

Can be found

in this example in the langchain-mcp-tools-ts-usage repo

Building from Source

See README_DEV.md for details.

Introduction

This package is intended to simplify the use of

Model Context Protocol (MCP)

server tools with LangChain / TypeScript.

Model Context Protocol (MCP) is the de facto industry standard

that dramatically expands the scope of LLMs by enabling the integration of external tools and resources,

including DBs, Cloud Storages, GitHub, Docker, Slack, and more.

There are quite a few useful MCP servers already available.

See MCP Server Listing on the Official Site.

This utility's goal is to make these numerous MCP servers easily accessible from LangChain.

It contains a utility function `convertMcpToLangchainTools()`.

This async function handles parallel initialization of specified multiple MCP servers

and converts their available tools into an array of LangChain-compatible tools.

It also performs LLM provider-specific schema transformations

to prevent schema compatibility issues

For detailed information on how to use this library, please refer to the following document:

"Supercharging LangChain: Integrating 2000+ MCP with ReAct".

MCP Protocol Support

This library supports MCP Protocol version 2025-03-26 and maintains backwards compatibility with version 2024-11-05.

It follows the official MCP specification for transport selection and backwards compatibility.

This library does not support the MCP 2026-07-28 specification.

The 2026-07-28 update requires substantial changes to MCP client behavior and

the TypeScript SDK integration, and this project is not actively tracking those

new protocol features.

For new MCP integrations with LangChain, use

LangChain's official LangChain.js MCP Adapters library.

Limitations

• Tool Return Types: Currently, only text results of tool calls are supported.

The library uses LangChain's `response_format: 'content'` (the default), which only supports text strings.

While MCP tools can return multiple content types (text, images, etc.), this library currently filters and uses only text content.

• MCP Features: Only MCP Tools are supported. Other MCP features like Resources, Prompts, and Sampling are not implemented.

Notes

• LLM Compatibility and Schema Transformations: The library can perform schema transformations for LLM compatibility.

See below for details.

• Passing PATH Env Variable: The library automatically adds the `PATH` environment variable to local server configrations if not explicitly provided to ensure servers can find required executables.

Features

Environment Variable Configuration for Local MCP Server

If you need to pass an API key or other configurations to a local MCP server

via environment variables, use this example as a guide:

brave: {
    command: "npx",
    args: [ "-y", "@modelcontextprotocol/server-brave-search"],
    env: { "BRAVE_API_KEY": process.env.BRAVE_API_KEY || "" }
  },

Note: The library automatically adds the `PATH` environment variable to local server configrations, if not explicitly provided, to ensure servers can find required executables.

`stderr` Redirection for Local MCP Server

A new key `"stderr"` has been introduced to specify a file descriptor to which local MCP server's stderr is redirected.

const logPath = `mcp-server-${serverName}.log`
const logFd = fs.openSync(logPath, 'w')
mcpServers[serverName].stderr = logFd

A usage example can be found here.

The key name `stderr` is derived from

TypeScript SDK's `StdioServerParameters`.

Working Directory Configuration for Local MCP Servers

The working directory that is used when spawning a local MCP server

can be specified with the `"cwd"` key as follows:

"local-server-name": {
      command: "...",
      args: [...],
      cwd: "/working/directory"  // the working dir to be use by the server
    },

The key name `cwd` is derived from

TypeScript SDK's `StdioServerParameters`.

Transport Selection Priority

The library selects transports using the following priority order:

1. Explicit transport/type field (must match URL protocol if URL provided)

2. URL protocol auto-detection (http/https → StreamableHTTP → SSE, ws/wss → WebSocket)

3. Command presence → Stdio transport (local MCP server)

4. Error if none of the above match

This ensures predictable behavior while allowing flexibility for different deployment scenarios.

Note: SSE transport is deprecated as of protocol version 2025-03-26; Streamable HTTP is the recommended approach.

Remote MCP Server Support

`mcp_servers` configuration for Streamable HTTP, SSE and Websocket servers are as follows:

// Auto-detection: tries Streamable HTTP first, falls back to SSE on 4xx errors
    "auto-detect-server": {
        url: `http://${server_host}:${server_port}/...`
    },

    // Explicit Streamable HTTP
    "streamable-http-server": {
        url: `http://${server_host}:${server_port}/...`,
        transport: "streamable_http"
        // type: "http"  // VSCode-style config also works instead of the above
    },

    // Explicit SSE (Note: SSE transport is deprecated)
    "sse-server-name": {
        url: `http://${sse_server_host}:${sse_server_port}/...`,
        transport: "sse"  // or `type: "sse"`
    },

    // WebSocket
    "ws-server-name": {
        url: `ws://${ws_server_host}:${ws_server_port}/...`
        // optionally `transport: "ws"` or `type: "ws"`
    },

The `"headers"` key can be used to pass HTTP headers to Streamable HTTP and SSE connection.

This header configuration will be overridden if either `streamableHTTPOptions` or `sseOptions` is specified (details below).

github: {
      // To avoid auto protocol fallback, specify the protocol explicitly when using authentication
      type: "http",  // or `transport: "http",`
      url: "https://api.githubcopilot.com/mcp/",
      headers: {
        "Authorization": `Bearer ${process.env.GITHUB_PERSONAL_ACCESS_TOKEN}`
      }
    },

Auto-detection behavior (default):

• For HTTP/HTTPS URLs without explicit `transport`, the library follows MCP specification recommendations
• First attempts Streamable HTTP transport
• If Streamable HTTP fails with a 4xx error, automatically falls back to SSE transport
• Non-4xx errors (network issues, etc.) are re-thrown without fallback

Explicit transport selection:

• Set `transport: "streamable_http"` (or VSCode-style config `type: "http"`) to force Streamable HTTP (no fallback)
• Set `transport: "sse"` to force SSE transport (SSE transport is deprecated)
• WebSocket URLs (`ws://` or `wss://`) always use WebSocket transport

Streamable HTTP is the modern MCP transport that replaces the older HTTP+SSE transport. According to the official MCP documentation: "SSE as a standalone transport is deprecated as of protocol version 2025-03-26. It has been replaced by Streamable HTTP, which incorporates SSE as an optional streaming mechanism."

Accessing Remote MCP Servers with OAuth Quickly

If you need to use MCP servers that require OAuth, consider using **"mcp-remote"**.

notion: {
      command: "npx",
      args: ["-y", "mcp-remote", "https://mcp.notion.com/mcp"],
    },

Authentication Support for Streamable HTTP Connections

The library supports OAuth 2.1 authentication for Streamable HTTP connections:

import { OAuthClientProvider } from '@modelcontextprotocol/sdk/client/auth.js'

// Implement your own OAuth client provider
class MyOAuthProvider implements OAuthClientProvider {
  // Implementation details...
}

const mcpServers = {
  'secure-streamable-server': {
    url: 'https://secure-mcp-server.example.com/mcp',
    // To avoid auto protocol fallback, specify the protocol explicitly when using authentication
    transport: 'streamable_http', // or `type: "http",`
    streamableHTTPOptions: {
      // Provide an OAuth client provider
      authProvider: new MyOAuthProvider(),

      // Optionally customize HTTP requests
      requestInit: {
        headers: {
          'X-Custom-Header': 'custom-value',
        },
      },

      // Optionally configure reconnection behavior
      reconnectionOptions: {
        maxReconnectAttempts: 5,
        reconnectDelay: 1000,
      },
    },
  },
}

Test implementations are provided:

• Streamable HTTP Authentication Tests:
  • MCP client uses this library: streamable-http-auth-test-client.ts
  • Test MCP Server: streamable-http-auth-test-server.ts

Appendix

Troubleshooting

1. Enable debug logging: Set `logLevel: "debug"` to see detailed connection and execution logs

2. Check server stderr: For local MCP servers, use `stderr` redirection to capture server error output

3. Test explicit transports: Try forcing specific transport types to isolate auto-detection issues

4. Verify server independently: Refer to Debugging Section in MCP documentation

Debugging Authentication

1. Check your tokens/credentials - Most auth failures are due to expired or incorrect tokens

2. Verify token permissions - Some MCP servers require specific scopes (e.g., GitHub Copilot license)

3. Test with curl - Try a simple HTTP request to verify your auth setup:

curl -H "Authorization: Bearer your-token" https://api.example.com/mcp/

LLM Provider Schema Compatibility

Different LLM providers have incompatible JSON Schema requirements for function calling:

• OpenAI requires: Optional fields must be nullable (`.optional()` + `.nullable()`)

for function calling (based on Structured Outputs API requirements,

strict enforcement coming in future SDK versions)

• Google Gemini API: Rejects nullable fields and `$defs` references, requires strict OpenAPI 3.0 subset compliance
• Anthropic Claude and xAI Grok: Very relaxed schema requirements with no documented restrictions

Note: Google Vertex AI provides OpenAI-compatible endpoints that support more relaxed requirements.

Real-World Impact

This creates challenges for developers trying to create universal schemas across providers.

Some MCP servers generate schemas that don't satisfy all providers' requirements.

For example, the official Notion local MCP server @notionhq/notion-mcp-server (version 1.9.0) and the remote server (at "https://mcp.notion.com/mcp", mcp-remote 0.1.18) produces:

OpenAI Warnings:

Zod field at `#/definitions/read_file/properties/tail` uses `.optional()` without `.nullable()` which is not supported by the API. See: https://platform.openai.com/docs/guides/structured-outputs?api-mode=responses#all-fields-must-be-required
... followed by many more

Gemini Errors:

[GoogleGenerativeAI Error]: Error fetching from https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent: [400 Bad Request] Invalid JSON payload received. Unknown name "exclusiveMaximum" at 'tools[0].function_declarations[14].parameters.properties[1].value': Cannot find field.
... followed by many more

Solution

The new option, `llmProvider`, has been introduced for performing provider-specific JSON schema transformations:

const { tools, cleanup } = await convertMcpToLangchainTools(mcpServers, {
  llmProvider: 'openai', // Makes optional fields nullable
  // llmProvider: "google_gemini" // Applies Gemini's strict validation rules
  // llmProvider: "anthropic"     // No transformations needed
})

Features:

• Generates INFO-level logs when transformations are applied
• Helps users identify potential MCP server schema improvements
• Falls back to original schema when no `llmProvider` is specified

Provider-Specific Transformations

For other providers, try without specifying the option:

const { tools, cleanup } = await convertMcpToLangchainTools(mcpServers)

References

• OpenAI Function Calling
• Gemini API Schema Requirements
• Anthropic Tool Use

Debugging and Logging

The library provides configurable logging to help debug connection and tool execution issues:

// Configure log level
const { tools, cleanup } = await convertMcpToLangchainTools(mcpServers, {
  logLevel: 'debug',
})

// Use custom logger
class MyLogger implements McpToolsLogger {
  debug(...args: unknown[]) {
    console.log('[DEBUG]', ...args)
  }
  info(...args: unknown[]) {
    console.log('[INFO]', ...args)
  }
  warn(...args: unknown[]) {
    console.warn('[WARN]', ...args)
  }
  error(...args: unknown[]) {
    console.error('[ERROR]', ...args)
  }
}

const { tools, cleanup } = await convertMcpToLangchainTools(mcpServers, {
  logger: new MyLogger(),
})

Available log levels: `"fatal" | "error" | "warn" | "info" | "debug" | "trace"`

For Developers

See README_DEV.md

for more information about development and testing.

License

MIT License - see LICENSE file for details.

Contributing

Issues and

pull requests welcome!

In particular, please share any issues relating to the latest versions of LangChain and LLM models, as well as specific MCP servers.