MCP爆火,我们用 Python 能做什么?

大家好,我是花姐,专注于分享 Python 技术的公众号博主。今天我们来聊聊最近在 AI 开发圈里炸裂的一个新名词——MCP。你可能会想:“这是什么鬼?我又得学新东西了?”别急,花姐带你一步步搞懂 MCP,顺便教你用 Python 写个 Hello World,保证你不掉队,甚至能领先一步!
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什么是 MCP?

MCP,全称 Model Context Protocol(模型上下文协议),是由 Anthropic(Claude 背后的公司)推出的一个开放协议。它的作用就像是 USB-C 接口一样,提供了一个统一的标准,让 AI 模型可以快速接入各种数据源和工具。你只需要配置一次,AI 就能通过 MCP 与数据库、API、文件系统等进行通信,再也不用为每个工具写一堆重复的代码了。

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举个简单的例子:
假设我们有个机器学习模型,它用来预测股票价格。如果没有MCP,当你输入“今天股市开盘如何?”这个问题时,模型可能无法正确理解你的意思,因为它没有清楚的上下文来处理股市的实时信息。而有了MCP,它就能清楚知道今天是“股市开盘时间”,“大盘的最新行情”,“个股行情”,根据最新的市场数据给出合理的答案。

这样,MCP就好比是一把“钥匙”,让模型可以在不同的环境中解锁不同的任务。😉

MCP 的架构非常简单,分为三部分:

  • MCP 主机(Host):如 Claude、IDE、AI 助手等,发起与外部数据源或工具的交互。
  • MCP 客户端(Client)
### MCP in Python Usage and Implementation #### Overview of MCP in Python The Model Context Protocol (MCP) is a protocol designed to facilitate interactions between AI models and external tools, data sources, or APIs[^3]. In the context of Python, MCP can be implemented using the MCP Python SDK, which provides tools for building both servers and clients. This implementation allows developers to interact with MCP bridges or agents effectively. #### Installing MCP Python SDK To integrate MCP into Python projects, the MCP Python SDK can be installed via pip: ```bash pip install mcp ``` This command installs the necessary libraries for interacting with MCP servers or clients[^1]. #### Configuring MCP Server in Python A MCP server can be configured in Python by defining its behavior and endpoints. Below is an example of setting up a basic MCP server using Python: ```python from mcp.server import MCPServer def handle_request(data): # Process incoming request data return {"result": "Processed"} if __name__ == "__main__": server = MCPServer(handle_request, port=8080) server.start() ``` In this example, the `MCPServer` class initializes a server that listens on port 8080 and processes incoming requests by calling the `handle_request` function[^1]. #### Configuring MCP Client in Python For interacting with an existing MCP server, a client can be set up as follows: ```python from mcp.client import MCPClient client = MCPClient(mcp_url="http://localhost:8080", mcp_port=8080) response = client.send_request({"action": "fetch_data"}) print(response) ``` Here, the `MCPClient` sends a request to the MCP server at the specified URL and port, and retrieves the response[^2]. #### Advanced Configuration Options MCP servers and clients can be further customized with additional parameters such as JSON formatting, logging levels, and security settings. For instance: ```python client = MCPClient( mcp_url="http://localhost:8080", mcp_port=8080, hide_json=True, json_width=120 ) ``` This configuration hides JSON results from tool executions and sets the maximum width for JSON output to 120 characters. #### Integration with Databases MCP can also be integrated with databases to enhance data retrieval and model interaction. This approach offers advantages over traditional RAG methods by providing more efficient and precise data access[^4]. An example of integrating MCP with a database might look like this: ```python from mcp.server import MCPServer import sqlite3 def fetch_data_from_db(query): conn = sqlite3.connect("example.db") cursor = conn.cursor() cursor.execute(query) result = cursor.fetchall() conn.close() return result def handle_request(data): query = data.get("query") if query: return {"data": fetch_data_from_db(query)} return {"error": "No query provided"} if __name__ == "__main__": server = MCPServer(handle_request, port=8080) server.start() ``` This script sets up an MCP server that executes SQL queries against a SQLite database[^4]. #### Best Practices for MCP Implementation - Ensure secure communication between MCP clients and servers using authentication mechanisms. - Optimize performance by configuring appropriate logging levels and resource limits. - Test the MCP implementation thoroughly to handle edge cases and errors gracefully.
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