Model Context Protocol (MCP) Server

Name: Model Context Protocol (MCP) Server
Author: shaswata-das

@shaswata-das

リポジトリ

Model Context Protocol (MCP) Server について

概要はまだありません

#mcp-server #ai-services #json-rpc

基本情報

カテゴリ

その他

ライセンス

MIT license

ランタイム

python

トランスポート

stdio

公開者

shaswata-das

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": {
    "basic-mcp-server-shaswata-das": {
      "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 Model Context Protocol (MCP) Server?

A modular Model Context Protocol server for AI services that supports multiple AI providers (Claude, OpenAI, and a mock service) and transport options (stdio, TCP, and WebSocket). It provides a JSON‑RPC 2.0 interface for tool calling and resource management, with dynamic service selection on a per‑request basis.

How to use Model Context Protocol (MCP) Server?

Clone the repository, create a virtual environment, install dependencies with pip install -e ., and configure API keys via the .env file. Run the server with python mcp_server.py (defaults to stdio mode) or use --tcp or --websocket flags to choose other transports. Interact using the provided example clients (examples/example_client.py and examples/websocket_client.html).

Key features of Model Context Protocol (MCP) Server

Multiple AI services (Claude, OpenAI, mock) selectable per request
Three transport options: stdio, TCP, and WebSocket
JSON‑RPC 2.0 compliant interface for predictable interactions
Dynamic service selection without restarting the server
Streaming response support for compatible transports
Modular architecture for easy extension with new services and transports
Qdrant vector database integration for embeddings and semantic search

Use cases of Model Context Protocol (MCP) Server

Build AI‑powered command‑line tools that can switch between models at runtime
Integrate AI assistants into web browsers using WebSocket transport
Test and develop AI workflows locally with the mock service, then swap in real APIs
Deploy a single server that serves multiple AI backends behind a load balancer