LangChain's official LangChain MCP Adapters library has been released at:
- pypi: https://pypi.org/project/langchain-mcp-adapters/
- github: https://github.com/langchain-ai/langchain-mcp-adapters
You may want to consider using the above if you don't have specific needs for using this library...
This package is intended to simplify the use of Model Context Protocol (MCP) server tools with LangChain / Python.
Model Context Protocol (MCP), an open standard announced by Anthropic, dramatically expands LLM’s scope by enabling external tool and resource integration, including GitHub, Google Drive, Slack, Notion, Spotify, Docker, PostgreSQL, and more…
MCP is likely to become the de facto industry standard as OpenAI has announced its adoption.
Over 2000 functional components available as MCP servers:
- MCP Server Listing on the Official Site
- MCP.so - Find Awesome MCP Servers and Clients
- Smithery: MCP Server Registry
The goal of this utility is to make these 2000+ MCP servers readily accessible from LangChain.
It contains a utility function convert_mcp_to_langchain_tools()
.
This async function handles parallel initialization of specified multiple MCP servers
and converts their available tools into a list of LangChain-compatible tools.
For detailed information on how to use this library, please refer to the following document:
A typescript equivalent of this utility is available here
- Python 3.11+
pip install langchain-mcp-tools
Can be found here
A minimal but complete working usage example can be found in this example in the langchain-mcp-tools-py-usage repo
convert_mcp_to_langchain_tools()
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 dict
, e.g.:
mcp_servers = {
"filesystem": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-filesystem", "."]
},
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
tools, cleanup = await convert_mcp_to_langchain_tools(
mcp_servers
)
This utility function initializes all specified MCP servers in parallel,
and returns LangChain Tools
(tools: list[BaseTool]
)
by gathering available MCP tools from the servers,
and by wrapping them into LangChain tools.
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.:
# from langchain.chat_models import init_chat_model
llm = init_chat_model(
model="claude-3-7-sonnet-latest",
model_provider="anthropic"
)
# from langgraph.prebuilt import create_react_agent
agent = create_react_agent(
llm,
tools
)
For hands-on experimentation with MCP server integration, try this LangChain application built with the utility
For detailed information on how to use this library, please refer to the following document:
"Supercharging LangChain: Integrating 2000+ MCP with ReAct"
mcp_servers
configuration for SSE and Websocket servers are as follows:
"sse-server-name": {
"url": f"http://{sse_server_host}:{sse_server_port}/..."
},
"ws-server-name": {
"url": f"ws://{ws_server_host}:{ws_server_port}/..."
},
Note that the key "url"
may be changed in the future to match
the MCP server configurations used by Claude for Desktop once
it introduces remote server support.
A usage example can be found here
A new key "headers"
has been introduced to pass HTTP headers to the SSE (Server-Sent Events) connection.
It takes dict[str, str]
and is primarily intended to support SSE MCP servers
that require authentication via bearer tokens or other custom headers.
"sse-server-name": {
"url": f"http://{sse_server_host}:{sse_server_port}/..."
"headers": {"Authorization": f"Bearer {bearer_token}"}
},
The key name header
is derived from the Python SDK
sse_client()
argument name.
A simple example showing how to implement MCP SSE server and client with authentication can be found in sse-auth-test-client.py and in sse-auth-test-server.py of this usage examples repo.
The working directory that is used when spawning a local (stdio) 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
Python SDK's StdioServerParameters
.
A new key "errlog"
has been introduced to specify a file-like object
to which local (stdio) MCP server's stderr is redirected.
log_path = f"mcp-server-{server_name}.log"
log_file = open(log_path, "w")
mcp_servers[server_name]["errlog"] = log_file
A usage example can be found here
NOTE: Why the key name errlog
was chosen:
Unlike TypeScript SDK's StdioServerParameters
, the Python
SDK's StdioServerParameters
doesn't include stderr: int
.
Instead, it calls stdio_client()
with a separate argument
errlog: TextIO
.
I once included stderr: int
for
compatibility with the TypeScript version, but decided to
follow the Python SDK more closely.
- Currently, only text results of tool calls are supported.
- MCP features other than Tools are not supported.
Can be found here