使用Langchain调用集成模型上下文协议(MCP)服务

pip install langchain-mcp-adapters

# math_server.py 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")

python3 math_server.py

# Create server parameters for stdio connection from mcp import ClientSession, StdioServerParameters from mcp.client.stdio import stdio_client from langchain_mcp_adapters.tools import load_mcp_tools from langgraph.prebuilt import create_react_agent from langchain_ollama import ChatOllama import asyncio ​ model = ChatOllama(model='qwen2.5:72b') ​ server_params = StdioServerParameters( command="python", # Make sure to update to the full absolute path to your math_server.py file args=["math_server.py"], ) ​ async def run_agent(): async with stdio_client(server_params) as (read, write): async with ClientSession(read, write) as session: # Initialize the connection await session.initialize() ​ # Get tools tools = await load_mcp_tools(session) ​ # Create and run the agent agent = create_react_agent(model, tools) agent_response = await agent.ainvoke({"messages": "what's (3 + 5) x 12?"}) return agent_response ​ # Run the async function if __name__ == "__main__": result = asyncio.run(run_agent()) print(result)

pip install langchain-mcp-tools

# Standard library imports import asyncio import logging import os import sys ​ # Third-party imports try: from dotenv import load_dotenv from langchain.chat_models import init_chat_model from langchain.schema import HumanMessage from langchain_ollama import ChatOllama from langgraph.prebuilt import create_react_agent except ImportError as e: print(f'\nError: Required package not found: {e}') print('Please ensure all required packages are installed\n') sys.exit(1) ​ # Local application imports from langchain_mcp_tools import convert_mcp_to_langchain_tools ​ # A very simple logger def init_logger() -> logging.Logger: logging.basicConfig( level=logging.INFO, # logging.DEBUG, format='\x1b[90m[%(levelname)s]\x1b[0m %(message)s' ) return logging.getLogger() ​ async def run() -> None: # Be sure to set ANTHROPIC_API_KEY and/or OPENAI_API_KEY as needed load_dotenv() ​ # Check the api key early to avoid showing a confusing long trace if not os.environ.get('ANTHROPIC_API_KEY'): raise Exception('ANTHROPIC_API_KEY env var needs to be set') # if not os.environ.get('OPENAI_API_KEY'): # raise Exception('OPENAI_API_KEY env var needs to be set') ​ try: mcp_configs = { 'filesystem': { 'command': 'npx', 'args': [ '-y', '@modelcontextprotocol/server-filesystem', '.' # path to a directory to allow access to ] }, 'fetch': { 'command': 'uvx', 'args': [ 'mcp-server-fetch' ] }, 'weather': { 'command': 'npx', 'args': [ '-y', '@h1deya/mcp-server-weather' ] }, } tools, cleanup = await convert_mcp_to_langchain_tools( mcp_configs, init_logger() ) llm = ChatOllama(model='qwen2.5:72b') agent = create_react_agent( llm, tools ) # query = 'Read the news headlines on bbc.com' # query = 'Read and briefly summarize the LICENSE file' query = "Tomorrow's weather in SF?" print('\x1b[33m') # color to yellow print(query) print('\x1b[0m') # reset the color messages = [HumanMessage(content=query)] result = await agent.ainvoke({'messages': messages}) # the last message should be an AIMessage response = result['messages'][-1].content print('\x1b[36m') # color to cyan print(response) print('\x1b[0m') # reset the color finally: if cleanup is not None: await cleanup() def main() -> None: asyncio.run(run()) ​ if __name__ == '__main__': main()

{'messages': [HumanMessage(content="what's (3 + 5) x 12?", additional_kwargs={}, response_metadata={}, id='87a8b6b6-9add-4da7-aea5-1b197c0fc0f5'), AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_1eyRzR7WpKzhMXG4ZFQAJtUD', ​ 'function': {'arguments': '{"a": 3, "b": 5}', 'name': 'add'}, 'type': 'function'}, {'id': 'call_q82CX807NC3T6nHMrhoHT46E', ​ 'function': {'arguments': '{"a": 8, "b": 12}', 'name': 'multiply'}, 'type': 'function'}], ​ 'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 51, 'prompt_tokens': 77, 'total_tokens': 128, ​ 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, ​ 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, ​ 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_eb9dce56a8', 'finish_reason': 'tool_calls', 'logprobs': None}, ​ id='run-13c01640-f92b-48b7-9340-c2ad983eb1c8-0', tool_calls=[{'name': 'add', 'args': {'a': 3, 'b': 5}, 'id': 'call_1eyRzR7WpKzhMXG4ZFQAJtUD', 'type': 'tool_call'}, {'name': 'multiply', 'args': {'a': 8, 'b': 12}, 'id': 'call_q82CX807NC3T6nHMrhoHT46E', 'type': 'tool_call'}], ​ usage_metadata={'input_tokens': 77, 'output_tokens': 51, 'total_tokens': 128, 'input_token_details': {'audio': 0, 'cache_read': 0}, ​ 'output_token_details': {'audio': 0, 'reasoning': 0}}), ToolMessage(content='8', name='add', id='f8e0aba5-7a62-44c6-92a3-5fe3b07c9bd5', tool_call_id='call_1eyRzR7WpKzhMXG4ZFQAJtUD'), ​ ToolMessage(content='96', name='multiply', id='66b9bbd9-b99a-402f-b26c-df83f5a69fa3', tool_call_id='call_q82CX807NC3T6nHMrhoHT46E'), AIMessage(content='The result of \\((3 + 5) \\times 12\\) is 96.', ​ additional_kwargs={'refusal': None}, ​ response_metadata={'token_usage': {'completion_tokens': 22, 'prompt_tokens': 143, 'total_tokens': 165, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, ​ 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, ​ 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_eb9dce56a8', 'finish_reason': 'stop', 'logprobs': None}, ​ id='run-6c00a336-7d52-4917-9186-b282a5984b10-0', usage_metadata={'input_tokens': 143, 'output_tokens': 22, 'total_tokens': 165, 'input_token_details': {'audio': 0, 'cache_read': 0}, ​ 'output_token_details': {'audio': 0, 'reasoning': 0}})]}