MCP Server Implementation

Name: MCP Server Implementation
Author: MCP-Mirror

@MCP-Mirror

代码仓库

关于 MCP Server Implementation

Mirror of

#mcp #mcp-server #flask #llm #api

基本信息

分类

其他

运行时

python

传输方式

stdio

发布者

MCP-Mirror

配置

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

未检测到工具

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。

概览

What is MCP Server Implementation?

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.

How to use MCP Server Implementation?

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.

Key features of MCP Server Implementation

Full MCP parsing, execution, and response handling
Sample tools: weather and calculator with parameter validation
Maintains conversation context across multiple interactions
Regex-based parsing for flexible tool invocation detection
Flask REST API for chat integration

Use cases of MCP Server Implementation

Enhance an LLM with real-time weather data retrieval
Perform mathematical calculations through natural language queries
Extend LLM capabilities with custom tools by inheriting the Tool class
Build conversational AI applications that seamlessly invoke external services

FAQ from MCP Server Implementation

What is MCP and how does it differ from function calling?

MCP (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.

What dependencies does the server require?

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.

How does conversation state persist?

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.

Are there known limitations?

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.

What authentication is required?

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.