MCP Server Implementation

Name: MCP Server Implementation
Author: MCP-Mirror

@MCP-Mirror

リポジトリ

MCP Server Implementation について

Mirror of

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

基本情報

カテゴリ

その他

ランタイム

python

トランスポート

stdio

公開者

MCP-Mirror

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "yisu201506_mcp_server": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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.