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MCP Server

@eooo-io

基本情報

カテゴリ

クラウドとインフラ

トランスポート

stdio

作者

eooo-io

MCP Server について

A Dockerized MCP server setup to deploy onto remote development servers

MCP Server の設定

標準の設定はありません

このサーバーの README には解析可能な MCP 設定ブロックが含まれていません。インストール手順はリポジトリをご確認ください。

リポジトリ

MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

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 Server?

A containerized Model Context Protocol (MCP) server that enables communication between LLMs and tools through a standardized protocol. It provides both REST API and WebSocket interfaces for tool execution and context management, targeting developers building LLM-powered applications.

How to use Model Context Protocol Server?

Deploy the server using Docker Compose: clone the repository, create tools, context, and data directories, configure a .env file, then run docker-compose up --build. Tools are placed as Python scripts in the tools directory, and the server exposes REST endpoints on port 3000 and a WebSocket at ws://localhost:3000 for real‑time communication.

Key features of Model Context Protocol Server

  • Dual runtime environment (Node.js + Python)
  • WebSocket support for real‑time updates
  • REST API endpoints for tool management and execution
  • Context persistence and management via JSON files
  • Tool execution isolation inside the container
  • Easy deployment with Docker

Use cases of Model Context Protocol Server

  • Allow LLMs to invoke Python‑based tools through a standardized API
  • Maintain conversation or session context across multiple interactions
  • Enable real‑time tool execution and monitoring via WebSocket
  • Provide a consistent, containerized interface for integrating tools with any MCP‑compatible LLM

よくある質問

1

What are the prerequisites to run Model Context Protocol Server?

You need Docker and Docker Compose installed on your system.

2

How do I add a new tool to the server?

Place a Python script in the `tools` directory. The script should accept JSON parameters via `sys.argv[1]` and return JSON output printed to stdout.

3

How is context managed in Model Context Protocol Server?

Context files are stored as JSON in the `context` directory, each with a unique ID. They can be retrieved via the `GET /api/context/:id` REST endpoint or through WebSocket messages.

4

How do I stop the server?

Run `docker-compose down` in the project directory.

5

What security measures are implemented?

Tool execution is isolated within the container, input validation is performed on all API endpoints, and context access can be restricted based on implementation needs.

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