LangChain MCP Adapters Skill

Expert assistance for langchain-mcp-adapters: the official LangChain bridge to MCP (Model Context Protocol) servers. Converts MCP tools, prompts, and resources into LangChain-native objects usable by any LangChain agent or chain.

Install: pip install langchain-mcp-adapters

Reference: references/api.md (500 KB — full API reference) and references/llms.md (28 KB — index).

When to Use This Skill

Activate when:

• Connecting to MCP servers — using MultiServerMCPClient or create_session() to connect via stdio, SSE, HTTP, or WebSocket
• Loading MCP tools — calling loadmcptools() or client.get_tools() to get BaseTool-compatible tools
• Configuring connection types — choosing between StdioConnection, SSEConnection, StreamableHttpConnection, WebsocketConnection
• Adding tool interceptors — implementing ToolCallInterceptor for retry, caching, rate limiting, or auth
• Handling MCP callbacks — using LoggingMessageCallback, ProgressCallback, or ElicitationCallback
• Loading MCP resources or prompts — calling loadmcpresources(), getmcpresource(), or loadmcpprompt()
• Prefixing tool names — avoiding name collisions across multiple MCP servers with toolnameprefix=True
• Converting to FastMCP — using to_fastmcp() to expose LangChain tools as a FastMCP server

Quick Reference

Connect to multiple MCP servers and load tools

from langchain_mcp_adapters.client import MultiServerMCPClient

async with MultiServerMCPClient(
    connections={
        "filesystem": {
            "transport": "stdio",
            "command": "npx",
            "args": ["-y", "@modelcontextprotocol/server-filesystem", "/tmp"],
        },
        "weather": {
            "transport": "streamable_http",
            "url": "https://weather-mcp.example.com/mcp",
        },
    },
    tool_name_prefix=True,   # tools become "filesystem_read_file", "weather_search"
) as client:
    tools = await client.get_tools()
    # Use tools with any LangChain agent
    agent = create_react_agent(llm, tools)

Connection types

from langchain_mcp_adapters.sessions import (
    StdioConnection, SSEConnection, StreamableHttpConnection, WebsocketConnection
)

# stdio — local process (most common for CLI tools)
stdio: StdioConnection = {
    "transport": "stdio",
    "command": "python",
    "args": ["-m", "my_mcp_server"],
    "env": {"MY_API_KEY": "..."},
    "cwd": "/path/to/server",
}

# StreamableHTTP — remote server (recommended for network)
http: StreamableHttpConnection = {
    "transport": "streamable_http",
    "url": "https://my-mcp-server.example.com/mcp",
    "headers": {"Authorization": "Bearer my-token"},
    "timeout": timedelta(seconds=30),
}

# SSE — legacy remote (use streamable_http for new servers)
sse: SSEConnection = {
    "transport": "sse",
    "url": "https://my-mcp-server.example.com/sse",
}

Load tools from a single session

from langchain_mcp_adapters.sessions import create_session
from langchain_mcp_adapters.tools import load_mcp_tools

async with create_session(connection) as session:
    tools: list[BaseTool] = await load_mcp_tools(
        session=session,
        server_name="my_server",
        tool_name_prefix=True,   # prefix tool names with server_name
    )

Tool call interceptors (middleware / onion pattern)

from langchain_mcp_adapters.interceptors import ToolCallInterceptor, MCPToolCallRequest, MCPToolCallResult

class RateLimitInterceptor(ToolCallInterceptor):
    async def intercept(self, request: MCPToolCallRequest, handler) -> MCPToolCallResult:
        await rate_limiter.acquire()
        return await handler(request)   # call next interceptor or actual tool

class RetryInterceptor(ToolCallInterceptor):
    async def intercept(self, request: MCPToolCallRequest, handler) -> MCPToolCallResult:
        for attempt in range(3):
            try:
                return await handler(request)
            except Exception:
                if attempt == 2:
                    raise

# First interceptor = outermost layer
client = MultiServerMCPClient(
    connections={...},
    tool_interceptors=[RateLimitInterceptor(), RetryInterceptor()],
)

Callbacks

from langchain_mcp_adapters.callbacks import LoggingMessageCallback, ProgressCallback

# Log all MCP messages
logging_cb = LoggingMessageCallback()

# Track progress of long-running operations
progress_cb = ProgressCallback(on_progress=lambda p: print(f"Progress: {p}%"))

client = MultiServerMCPClient(
    connections={...},
    callbacks=[logging_cb, progress_cb],
)

Load MCP resources and prompts

from langchain_mcp_adapters.resources import load_mcp_resources, get_mcp_resource
from langchain_mcp_adapters.prompts import load_mcp_prompt

async with create_session(connection) as session:
    # Load all resources as LangChain Blobs
    resources = await load_mcp_resources(session)

    # Get a specific resource
    blob = await get_mcp_resource(session, uri="file:///data/config.json")

    # Load a prompt and convert to LangChain messages
    messages = await load_mcp_prompt(session, name="summarize", arguments={"text": "..."})

Convert LangChain tools to FastMCP server

from langchain_mcp_adapters.tools import to_fastmcp
from langchain_community.tools import DuckDuckGoSearchRun

lc_tools = [DuckDuckGoSearchRun()]
fastmcp_server = to_fastmcp(lc_tools)   # expose as MCP server
fastmcp_server.run()

API Reference

MultiServerMCPClient

MultiServerMCPClient(
    connections: dict[str, Connection] | None = None,
    callbacks: Callbacks | None = None,
    tool_interceptors: list[ToolCallInterceptor] | None = None,
    tool_name_prefix: bool = False,
)

Connection types

Key functions

Reference Files

Source: https://reference.langchain.com/python/langchain-mcp-adapters GitHub: https://github.com/langchain-ai/langchain-mcp-adapters