langgraph/docs/docs/agents/mcp.md

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Use MCP

The Model Context Protocol (MCP) is an open protocol that standardizes how applications provide tools and context to language models. LangGraph agents can use tools defined on MCP servers through the langchain-mcp-adapters library.

Use MCP tools

The langchain-mcp-adapters package enables agents to use tools defined across one or more MCP servers.

=== "In an agent"

```python title="Agent using tools defined on MCP servers"
# highlight-next-line
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent

# highlight-next-line
client = MultiServerMCPClient(
    {
        "math": {
            "command": "python",
            # Replace with absolute path to your math_server.py file
            "args": ["/path/to/math_server.py"],
            "transport": "stdio",
        },
        "weather": {
            # Ensure you start your weather server on port 8000
            "url": "http://localhost:8000/mcp",
            "transport": "streamable_http",
        }
    }
)
# highlight-next-line
tools = await client.get_tools()
agent = create_react_agent(
    "anthropic:claude-3-7-sonnet-latest",
    # highlight-next-line
    tools
)
math_response = await agent.ainvoke(
    {"messages": [{"role": "user", "content": "what's (3 + 5) x 12?"}]}
)
weather_response = await agent.ainvoke(
    {"messages": [{"role": "user", "content": "what is the weather in nyc?"}]}
)
```

=== "In a workflow"

```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.prebuilt import ToolNode, tools_condition

from langchain.chat_models import init_chat_model
model = init_chat_model("openai:gpt-4.1")

client = MultiServerMCPClient(
    {
        "math": {
            "command": "python",
            # Make sure to update to the full absolute path to your math_server.py file
            "args": ["./examples/math_server.py"],
            "transport": "stdio",
        },
        "weather": {
            # make sure you start your weather server on port 8000
            "url": "http://localhost:8000/mcp/",
            "transport": "streamable_http",
        }
    }
)
tools = await client.get_tools()

def call_model(state: MessagesState):
    response = model.bind_tools(tools).invoke(state["messages"])
    return {"messages": response}

builder = StateGraph(MessagesState)
builder.add_node(call_model)
builder.add_node(ToolNode(tools))
builder.add_edge(START, "call_model")
builder.add_conditional_edges(
    "call_model",
    tools_condition,
)
builder.add_edge("tools", "call_model")
graph = builder.compile()
math_response = await graph.ainvoke({"messages": "what's (3 + 5) x 12?"})
weather_response = await graph.ainvoke({"messages": "what is the weather in nyc?"})
```

Custom MCP servers

To create your own MCP servers, you can use the mcp library. This library provides a simple way to define tools and run them as servers.

Install the MCP library:

pip install mcp

Use the following reference implementations to test your agent with MCP tool servers.

from mcp.server.fastmcp import FastMCP

mcp = FastMCP("Math")

@mcp.tool()
def add(a: int, b: int) -> int:
    """Add two numbers"""
    return a + b

@mcp.tool()
def multiply(a: int, b: int) -> int:
    """Multiply two numbers"""
    return a * b

if __name__ == "__main__":
    mcp.run(transport="stdio")

from mcp.server.fastmcp import FastMCP

mcp = FastMCP("Weather")

@mcp.tool()
async def get_weather(location: str) -> str:
    """Get weather for location."""
    return "It's always sunny in New York"

if __name__ == "__main__":
    mcp.run(transport="streamable-http")

Additional resources

• MCP documentation
• MCP Transport documentation
• langchain_mcp_adapters