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使用 MCP

模型上下文协议 (MCP) 是一种开放协议，它规范了应用程序如何向语言模型提供工具和上下文。LangGraph 智能体可以通过 langchain-mcp-adapters 库来使用在 MCP 服务器上定义的工具。

MCP

安装 langchain-mcp-adapters 库以在 LangGraph 中使用 MCP 工具

pip install langchain-mcp-adapters

使用 MCP 工具

langchain-mcp-adapters 包使智能体能够使用定义在一个或多个 MCP 服务器上的工具。

使用 MCP 服务器上定义的工具的智能体
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent

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": "https://:8000/mcp",
            "transport": "streamable_http",
        }
    }
)
tools = await client.get_tools()
agent = create_react_agent(
    "anthropic:claude-3-7-sonnet-latest",
    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?"}]}
)
使用 ToolNode 的 MCP 工具工作流
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.chat_models import init_chat_model
from langgraph.graph import StateGraph, MessagesState, START, END
from langgraph.prebuilt import ToolNode

# Initialize the model
model = init_chat_model("anthropic:claude-3-5-sonnet-latest")

# Set up MCP client
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": "https://:8000/mcp/",
            "transport": "streamable_http",
        }
    }
)
tools = await client.get_tools()

# Bind tools to model
model_with_tools = model.bind_tools(tools)

# Create ToolNode
tool_node = ToolNode(tools)

def should_continue(state: MessagesState):
    messages = state["messages"]
    last_message = messages[-1]
    if last_message.tool_calls:
        return "tools"
    return END

# Define call_model function
async def call_model(state: MessagesState):
    messages = state["messages"]
    response = await model_with_tools.ainvoke(messages)
    return {"messages": [response]}

# Build the graph
builder = StateGraph(MessagesState)
builder.add_node("call_model", call_model)
builder.add_node("tools", tool_node)

builder.add_edge(START, "call_model")
builder.add_conditional_edges(
    "call_model",
    should_continue,
)
builder.add_edge("tools", "call_model")

# Compile the graph
graph = builder.compile()

# Test the graph
math_response = await graph.ainvoke(
    {"messages": [{"role": "user", "content": "what's (3 + 5) x 12?"}]}
)
weather_response = await graph.ainvoke(
    {"messages": [{"role": "user", "content": "what is the weather in nyc?"}]}
)

自定义 MCP 服务器

要创建自己的 MCP 服务器，您可以使用 mcp 库。该库提供了一种定义工具并将其作为服务器运行的简单方法。

安装 MCP 库

pip install mcp

使用以下参考实现来测试您的智能体与 MCP 工具服务器的交互。

数学服务器示例 (stdio 传输)
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")
天气服务器示例 (可流式 HTTP 传输)
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")

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