Discover Awesome MCP Servers

Claude MCP Server Collection

「Claude のための MCP サーバー実装」

LND MCP Server

自然言語を用いてLightning Network (LND) ノードのデータを問い合わせるためのモデルコンテキストプロトコル (MCP)。

MCP Atlassian

Confluence と Jira を統合する MCP サーバー

MCP Connector for Open WebUI

Open WebUI を MCP (Model Context Protocol) サーバーに接続する

Figma MCP Server

AIツールやLLMをFigmaのデザインに接続し、デザインデータの抽出、デザインシステムの分析、開発ドキュメントの生成を可能にする、モデルコンテキストプロトコルサーバー。

Azure Revisor MCP Server

Cursor IDEと連携し、Azureリポジトリのコードレビュー機能を提供するサーバー。AIを活用したプルリクエストの分析とフィードバックを可能にする。

MCP Custom Server

Model Context Protocol TypeScript SDK を使用したカスタム MCP サーバー実装

simple-mcp-server

シンプルな MCP サーバー

SQLite MCP Server

SQL操作（SELECT、INSERT、UPDATE、DELETE）とテーブル管理を、標準化されたインターフェースを通じてSQLiteデータベースで可能にする、モデルコンテキストプロトコルサーバー。

ClickUp MCP Server

ClickUp MCPサーバーを使用すると、AIアシスタントがClickUpワークスペースとシームレスに連携できます。この強力な統合により、Claudeは自然な会話を通じて、タスクの作成と管理、ドキュメントへのアクセス、フォルダーとリストの整理、コメントの追加、チェックリストの処理などを行うことができます。

Rust Docs MCP Server

鏡 (Kagami)

MCP Reporter

mcp-reporter は、Model Context Protocol サーバー用の包括的な機能レポートを生成する合理化されたユーティリティです。開発者は、ドキュメント作成と他のツールへの統合の両方のために、MCP サーバーのエコシステム全体で利用可能な機能を簡単に理解できます。

test-server MCP server

鏡 (Kagami)

Goal Story MCP Server

AIを活用した目標管理システム。従来の目標追跡をストーリーテリングに変え、パーソナライズされた物語と洞察によって、ユーザーが一度に一つの目標に集中できるよう支援し、モチベーションと達成度を高めます。

MCP Server for Ticketmaster

鏡 (Kagami)

Code Index MCP

大規模言語モデルが、最小限のセットアップでコードリポジトリのインデックス作成、検索、および分析を行うのに役立つ、Model Context Protocol（MCP）サーバー

Filesys 📁

ファイルシステムMCPサーバー：Model Context Protocolを介して、LLMがローカルマシン上の指定されたディレクトリからファイルを読み取り、一覧表示できるようにします。

MCP server proxy

Okay, I understand. You want to extend the functionality of an MCP (Minecraft Protocol) server to act as a worker in a distributed system. This means you want the MCP server to be able to receive tasks, process them (likely related to Minecraft), and return results. Here's a breakdown of the concepts, potential approaches, and considerations for achieving this: **1. Understanding the Core Concepts** * **MCP Server:** This is your existing Minecraft server, likely built using a library like `minecraft-server` or a similar framework that handles the Minecraft protocol. It currently manages players, world state, and game logic. * **Worker:** In this context, a worker is a process or service that receives tasks from a central system (a task queue, a scheduler, etc.), performs those tasks, and reports the results. * **Task Queue/Scheduler:** This is the central component that distributes tasks to available workers. Examples include: * **RabbitMQ:** A robust message broker. * **Celery (Python):** A distributed task queue. * **Redis:** Can be used as a simple task queue. * **Custom Solution:** You could build your own task queue using a database or other mechanism. * **Task Definition:** A task is a unit of work. For example: * "Generate a specific chunk of the world." * "Find the nearest diamond ore to coordinates X, Y, Z." * "Simulate the growth of a crop over a period of time." * "Execute a specific command on the server." * **Serialization/Deserialization:** You'll need a way to convert tasks and results into a format that can be sent over the network (e.g., JSON, Protocol Buffers). **2. Potential Approaches** Here are a few ways to integrate the MCP server as a worker, ranked from simpler to more complex: * **A. Command-Based Integration (Simplest):** * **Concept:** The task queue sends commands to the MCP server via the Minecraft protocol itself (e.g., using the `/execute` command or a custom plugin). * **Implementation:** 1. **Plugin/Mod:** Create a plugin or mod for your MCP server that listens for specific commands. 2. **Task Queue:** The task queue sends these commands to a designated player (or a "bot" account) on the server. 3. **Command Processing:** The plugin executes the command and captures the output. 4. **Result Reporting:** The plugin sends the result back to the task queue (e.g., via a separate HTTP endpoint or by sending another command). * **Pros:** Relatively easy to implement, leverages existing Minecraft protocol. * **Cons:** Limited by the capabilities of Minecraft commands, potential security risks if not carefully implemented, can be slow due to protocol overhead. * **B. Direct API Integration (More Flexible):** * **Concept:** Expose an API (e.g., a REST API) on the MCP server that allows external systems to trigger specific actions. * **Implementation:** 1. **API Server:** Embed a web server (e.g., using Flask, FastAPI, or Node.js) within your MCP server process. 2. **API Endpoints:** Create API endpoints that correspond to the tasks you want to perform (e.g., `/generate_chunk`, `/find_diamonds`). 3. **Task Queue:** The task queue sends HTTP requests to these endpoints with the task parameters. 4. **Task Processing:** The API endpoint executes the task within the MCP server's environment. 5. **Result Reporting:** The API endpoint returns the result as a JSON response. * **Pros:** More flexible than command-based integration, allows for more complex tasks. * **Cons:** Requires more development effort, needs careful security considerations. * **C. Dedicated Worker Process (Most Robust):** * **Concept:** Create a separate worker process that interacts with the MCP server's internal data structures directly (if possible) or through a well-defined API. * **Implementation:** 1. **Worker Process:** Develop a separate application (e.g., in Python, Java, or Go) that acts as the worker. 2. **Task Queue:** The task queue sends tasks to the worker process. 3. **MCP Server Interaction:** The worker process connects to the MCP server (either directly to its internal data structures, if accessible, or through a dedicated API). 4. **Task Processing:** The worker process performs the task using the MCP server's resources. 5. **Result Reporting:** The worker process sends the result back to the task queue. * **Pros:** Most robust and scalable, allows for the most complex tasks, can be optimized for performance. * **Cons:** Most complex to implement, requires a deep understanding of the MCP server's internals. **3. Key Considerations** * **Concurrency:** Minecraft servers are often single-threaded or have limited multi-threading capabilities. You'll need to carefully manage concurrency to avoid performance bottlenecks or crashes. Consider using asynchronous programming techniques (e.g., `asyncio` in Python) or offloading tasks to separate threads or processes. * **Resource Management:** Tasks can consume significant resources (CPU, memory, disk I/O). Implement resource limits and monitoring to prevent tasks from overloading the server. * **Security:** If you're exposing an API, ensure that it's properly secured to prevent unauthorized access. Use authentication, authorization, and input validation. * **Error Handling:** Implement robust error handling to gracefully handle task failures. Retry failed tasks, log errors, and provide informative error messages. * **Task Prioritization:** If you have different types of tasks, consider implementing task prioritization to ensure that important tasks are processed first. * **Data Consistency:** If tasks modify the world state, ensure that the changes are properly synchronized to avoid data inconsistencies. * **MCP Server Architecture:** The specific architecture of your MCP server implementation will heavily influence the best approach. If it's based on a well-defined API or plugin system, integration will be easier. If it's a more monolithic codebase, you may need to modify the core server code. * **Minecraft Protocol Limitations:** Be aware of the limitations of the Minecraft protocol. Some tasks may not be possible to perform directly through the protocol. **4. Example Scenario (API Integration - Approach B)** Let's say you want to implement a task to generate a specific chunk of the world. 1. **MCP Server (with API):** ```python from flask import Flask, request, jsonify import minecraft_server # Your MCP server library app = Flask(__name__) mcp_server = minecraft_server.MinecraftServer() # Initialize your server @app.route('/generate_chunk', methods=['POST']) def generate_chunk(): data = request.get_json() x = data.get('x') z = data.get('z') if x is None or z is None: return jsonify({'error': 'Missing x or z coordinate'}), 400 try: chunk_data = mcp_server.generate_chunk(x, z) # Call your chunk generation function return jsonify({'chunk_data': chunk_data}) except Exception as e: return jsonify({'error': str(e)}), 500 if __name__ == '__main__': mcp_server.start() # Start the minecraft server app.run(debug=True, port=5000) # Start the API server ``` 2. **Task Queue (using Celery):** ```python from celery import Celery import requests celery_app = Celery('tasks', broker='redis://localhost:6379/0') # Configure Celery @celery_app.task def generate_chunk_task(x, z): url = 'http://your_mcp_server_ip:5000/generate_chunk' data = {'x': x, 'z': z} response = requests.post(url, json=data) if response.status_code == 200: return response.json() else: raise Exception(f"Chunk generation failed: {response.text}") # Example usage: result = generate_chunk_task.delay(10, 20) # Enqueue the task print(f"Task ID: {result.id}") # Later, retrieve the result: # chunk_data = result.get() ``` **5. Steps to Implement** 1. **Choose an Approach:** Select the approach that best suits your needs and the architecture of your MCP server. 2. **Set up a Task Queue:** Choose a task queue system (RabbitMQ, Celery, Redis, etc.) and configure it. 3. **Implement the Worker Logic:** Write the code that will run on the MCP server to process tasks. This will involve creating API endpoints, plugins, or modifying the core server code. 4. **Implement the Task Queue Integration:** Write the code that will enqueue tasks and retrieve results from the task queue. 5. **Test Thoroughly:** Test your integration thoroughly to ensure that it's working correctly and that it's handling errors gracefully. 6. **Monitor Performance:** Monitor the performance of your system to identify bottlenecks and optimize performance. **Important Notes:** * Replace placeholders like `minecraft_server`, `your_mcp_server_ip`, and `redis://localhost:6379/0` with your actual values. * This is a high-level overview. The specific implementation details will depend on your MCP server and the task queue system you choose. * Security is paramount. Always validate input and protect your API endpoints. By carefully considering these factors and following a structured approach, you can successfully extend your MCP server to function as a worker in a distributed system. Good luck!

MCP Analytics Middleware

MCP SDKサーバー向けの軽量なTypeScriptミドルウェアで、分析機能を提供します。リクエストメトリクス、パフォーマンスデータ、および利用パターンを最小限のオーバーヘッドでキャプチャします。リアルタイム監視、設定可能なデータ収集、詳細なレポート機能を備えており、すべて完全な型安全性で実現されています。

Simple Weather MCP Server example from Quickstart

鏡 (Kagami)

MCP Server Filesystem Service

MCP ファイルシステムソリューション

Upstash MCP Server

鏡 (Kagami)

MCP Manager

MCPサーバーを管理するためのシンプルなGUI。MCPサーバーの簡単な切り替えが可能です。

mcp-dice: A MCP Server for Rolling Dicemcp-dice: A MCP Server for Rolling Dice

LLM (大規模言語モデル) がサイコロを振れるようにする MCP サーバー