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Frameworks & SDKs

How to Connect LangGraph to MewCP

LangGraph runs MCP tools through LangChain's MCP adapter. The adapter turns MewCP's tools into LangChain tools, and a ToolNode in your graph runs them. Your graph then reaches every app connected to its toolset.

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Before you start

• Python 3.10 or newer

• A toolset and its key. See Choose a toolset

• Your environment set as in Set your environment:

  Code
  MEWCP_MCP_URL=https://gateway.mewcp.com/personal/mcp
  MEWCP_API_KEY=API_KEY

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Install

Code
pip install "langchain[mcp]>=1.4.0" langgraph langchain-anthropic

langchain.mcp needs langchain[mcp] 1.4.0 or newer. The examples use an Anthropic model; any chat model LangChain supports works the same way.

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Connect

Code
import asyncio
import os

from fastmcp.client import Client
from langchain.chat_models import init_chat_model
from langchain.mcp import MCPAdapter
from langgraph.graph import START, MessagesState, StateGraph
from langgraph.prebuilt import ToolNode, tools_condition


async def build_graph():
    client = Client(os.environ["MEWCP_MCP_URL"], auth=os.environ["MEWCP_API_KEY"])
    async with MCPAdapter(client) as adapter:
        tools = await adapter.list_tools()

    model = init_chat_model("claude-sonnet-5").bind_tools(tools)

    async def agent(state: MessagesState):
        return {"messages": [await model.ainvoke(state["messages"])]}

    graph = StateGraph(MessagesState)
    graph.add_node("agent", agent)
    graph.add_node("tools", ToolNode(tools))
    graph.add_edge(START, "agent")
    graph.add_conditional_edges("agent", tools_condition)
    graph.add_edge("tools", "agent")
    return graph.compile()


async def main():
    graph = await build_graph()
    result = await graph.ainvoke(
        {"messages": [{"role": "user", "content": "What is on my calendar tomorrow?"}]}
    )
    print(result["messages"][-1].content)


asyncio.run(main())

auth= sends the key as Authorization: Bearer <key>. The tools stay usable after the async with block ends: each call opens its own connection to MewCP.

The loop between agent and tools lets the model run search, get_schema and call_tool one after another in a single answer.

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Serve your users

With a project toolset, send x-mewcp-enduser-id on every request, set to the user the graph is acting for. Build the client per user with a transport that carries the header:

Code
import os

from fastmcp.client import Client
from fastmcp.client.transports import StreamableHttpTransport
from langchain.mcp import MCPAdapter


async def tools_for(user_id: str):
    transport = StreamableHttpTransport(
        url=os.environ["MEWCP_MCP_URL"],
        headers={
            "Authorization": f"Bearer {os.environ['MEWCP_API_KEY']}",
            "x-mewcp-enduser-id": user_id,
        },
    )
    async with MCPAdapter(Client(transport)) as adapter:
        return await adapter.list_tools()

Build the graph from tools_for(user_id) for each user, as in Connect. On a LangGraph server, do it in your graph factory, where the run's authenticated user is available as runtime.user.identity.

Use the same ID you pass as externalUserId when the user connects their accounts through the MewCP Auth API. If they have not connected the app a tool needs, the tool result carries a link for them to connect it, and the model can pass it on.

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Check it works

Code
for tool in tools:
    print(tool.name)

You should see four tools: search, get_schema, list_accounts and call_tool. That is expected. See What your agent sees.

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In production

• Build per run on a LangGraph server. Build the graph inside your graph factory, so each run gets its own client and, with a project toolset, its own user.
• Pass the tool list once. Give the same tools list to both bind_tools and ToolNode, so the model can only call tools the node can run.

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Troubleshooting

ModuleNotFoundError: No module named 'langchain.mcp'

Install the MCP extra at 1.4.0 or newer: pip install "langchain[mcp]>=1.4.0". Older guides use the separate langchain-mcp-adapters package and MultiServerMCPClient. See LangChain's migration guide.

A LangChainBetaWarning is printed on import

langchain.mcp is in beta and warns once per process. The API may change between LangChain releases, so pin the version you test with.

Listing tools fails with 401 or 403

MewCP rejected the key.

• Check MEWCP_API_KEY is set where the code runs and holds the whole key.
• Check the key matches the toolset in MEWCP_MCP_URL: your API Key for the personal toolset, the project's key for a project toolset.
• A 403 that says the billing period is not active means your MewCP plan needs renewing in the dashboard.

A long task stops with GraphRecursionError

The graph hit its step limit before the model finished. Raise it for that run: await graph.ainvoke(inputs, {"recursion_limit": 50}).

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Last checked against LangChain's and LangGraph's documentation on 13 October 2026.