@MCP-Mirror
基本信息
Mirror of
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"yisu201506_mcp_server": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
MCP Server Implementation is a complete Flask-based implementation of the Model Context Protocol (MCP) for enhancing Large Language Model capabilities with external tools. It integrates with any text-based LLM by placing tool definitions directly in the model’s context window and parsing natural language responses to identify tool usage. This server is designed for developers who want to extend LLMs with custom tools like weather lookups or calculations without relying on API‑specific function‑calling features.
Clone the repository, create a virtual environment, install dependencies from requirements.txt, and set the required environment variables (LLM_API_KEY, WEATHER_API_KEY). Start the server with flask run (development) or gunicorn app:app (production). Send chat requests to the POST /chat endpoint with a JSON body containing a messages array. Alternatively, run the standalone example with python mcp_example.py.
Tool classMCP (Model Context Protocol) places tool definitions directly in the prompt text and uses natural language invocation, while function calling uses structured JSON in API parameters. MCP works with any text‑based LLM and is visible in the model’s response; function calling requires API support and may be hidden.
The server requires Python, Flask, gunicorn, and the packages listed in the `requirements.txt` file. Environment variables for an LLM API key and a weather API key must also be set.
The server maintains context across multiple interactions by adding processed responses (including tool results) to the conversation history, which is then included in subsequent LLM requests.
The implementation uses regex‑based parsing to identify tool calls in the LLM’s text output. Complex or malformed invocations may not be parsed correctly, and the server currently requires the LLM to output tool calls in a specific text format.
The server reads `LLM_API_KEY` and `WEATHER_API_KEY` from the environment (or a `.env` file) to access the LLM and weather API respectively. No additional authentication is implemented for the server itself.
This open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed for developers, it focuses on practical techniques for building modular, scalable,
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