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2.7 KiB
2.7 KiB
MCP Integration
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.
Install the langchain-mcp-adapters library to use MCP tools in LangGraph:
pip install langchain-mcp-adapters
Use MCP tools
The langchain-mcp-adapters package enables agents to use tools defined across one or more MCP servers.
# highlight-next-line
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
# highlight-next-line
async with MultiServerMCPClient(
{
"math": {
"command": "python",
# Replace with absolute path to your math_server.py file
"args": ["/path/to/math_server.py"],
"transport": "stdio",
},
"weather": {
# Ensure your start your weather server on port 8000
"url": "http://localhost:8000/sse",
"transport": "sse",
}
}
) as client:
agent = create_react_agent(
"anthropic:claude-3-7-sonnet-latest",
# highlight-next-line
client.get_tools()
)
math_response = await agent.ainvoke({"messages": "what's (3 + 5) x 12?"})
weather_response = await agent.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="sse")