Discover Awesome MCP Servers

github-readme-mcp

An MCP server that reviews README files using transparent, deterministic rules, scoring them and suggesting improvements.

AutoCAD MCP Server

Enables users to control AutoCAD, GstarCAD, or ZWCAD through natural language via Claude Desktop. It provides tools for drawing geometric shapes, adding text and dimensions, and managing CAD files using the Windows COM interface.

Tavily Search

一个集成了 Tavily 搜索 API 的 MCP 服务器实现，为 LLM 提供优化的搜索能力。

MCP 만들면서 원리 파헤쳐보기

Okay, here's a breakdown of the server and client implementation for a system conceptually similar to the MCP (Master Control Program) from the movie Tron, along with considerations for a modern implementation. Keep in mind that a real-world MCP would be incredibly complex, so this is a simplified, illustrative example. **Conceptual Overview** The MCP, in essence, is a central control system. In a modern context, we can think of it as a distributed system with the following key components: * **Server (MCP Core):** The central authority. It manages resources, schedules tasks, enforces security policies, and monitors the overall system health. * **Clients (Programs/Processes):** These are the individual applications or processes that interact with the MCP to request resources, execute tasks, and report their status. * **Communication Protocol:** A well-defined protocol for clients and the server to exchange information. **Implementation Considerations** * **Language Choice:** Python is a good choice for prototyping and scripting due to its readability and extensive libraries. For performance-critical components, consider languages like Go, Rust, or C++. * **Communication:** gRPC, ZeroMQ, or even a simple TCP socket-based protocol can be used for communication between the server and clients. gRPC is a modern, high-performance RPC framework that's well-suited for this kind of system. * **Security:** Authentication and authorization are crucial. Use strong authentication mechanisms (e.g., TLS certificates, API keys) and implement role-based access control (RBAC) to restrict access to sensitive resources. * **Resource Management:** The MCP needs to track available resources (CPU, memory, disk space, network bandwidth) and allocate them to clients based on their needs and priorities. * **Task Scheduling:** A scheduler determines the order in which tasks are executed. Prioritization, deadlines, and dependencies should be considered. * **Monitoring and Logging:** Comprehensive monitoring and logging are essential for detecting errors, performance bottlenecks, and security breaches. * **Fault Tolerance:** The MCP should be designed to be fault-tolerant. Consider using techniques like redundancy, replication, and failover to ensure that the system remains operational even if some components fail. **Simplified Python Example (Illustrative)** This is a very basic example to demonstrate the core concepts. It's not production-ready. ```python # server.py (MCP Core) import socket import threading import json import time HOST = '127.0.0.1' PORT = 65432 # Resource Management (very basic) available_cpu = 100 resource_lock = threading.Lock() def handle_client(conn, addr): print(f"Connected by {addr}") while True: try: data = conn.recv(1024).decode() if not data: break try: request = json.loads(data) print(f"Received request: {request}") if request['action'] == 'request_cpu': cpu_units = request['cpu_units'] with resource_lock: global available_cpu if available_cpu >= cpu_units: available_cpu -= cpu_units response = {'status': 'granted', 'cpu_units': cpu_units} print(f"Granted {cpu_units} CPU units. Remaining: {available_cpu}") else: response = {'status': 'denied', 'reason': 'Insufficient CPU'} conn.sendall(json.dumps(response).encode()) elif request['action'] == 'report_status': status = request['status'] print(f"Client reported status: {status}") conn.sendall(json.dumps({'status': 'received'}).encode()) elif request['action'] == 'release_cpu': cpu_units = request['cpu_units'] with resource_lock: available_cpu += cpu_units response = {'status': 'released', 'cpu_units': cpu_units} print(f"Released {cpu_units} CPU units. Remaining: {available_cpu}") conn.sendall(json.dumps(response).encode()) else: response = {'status': 'error', 'message': 'Invalid action'} conn.sendall(json.dumps(response).encode()) except json.JSONDecodeError: response = {'status': 'error', 'message': 'Invalid JSON'} conn.sendall(json.dumps(response).encode()) except ConnectionResetError: print(f"Connection reset by {addr}") break except Exception as e: print(f"Error handling client: {e}") break print(f"Closing connection with {addr}") conn.close() def main(): with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind((HOST, PORT)) s.listen() print(f"MCP Server listening on {HOST}:{PORT}") while True: conn, addr = s.accept() thread = threading.Thread(target=handle_client, args=(conn, addr)) thread.start() if __name__ == "__main__": main() ``` ```python # client.py (Program/Process) import socket import json import time HOST = '127.0.0.1' PORT = 65432 def request_cpu(conn, cpu_units): request = {'action': 'request_cpu', 'cpu_units': cpu_units} conn.sendall(json.dumps(request).encode()) response = json.loads(conn.recv(1024).decode()) return response def report_status(conn, status): request = {'action': 'report_status', 'status': status} conn.sendall(json.dumps(request).encode()) response = json.loads(conn.recv(1024).decode()) return response def release_cpu(conn, cpu_units): request = {'action': 'release_cpu', 'cpu_units': cpu_units} conn.sendall(json.dumps(request).encode()) response = json.loads(conn.recv(1024).decode()) return response def main(): with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.connect((HOST, PORT)) # Request CPU cpu_request_response = request_cpu(s, 20) print(f"CPU Request Response: {cpu_request_response}") if cpu_request_response['status'] == 'granted': # Report status status_response = report_status(s, 'Running task...') print(f"Status Response: {status_response}") time.sleep(5) # Simulate work # Release CPU cpu_release_response = release_cpu(s, 20) print(f"CPU Release Response: {cpu_release_response}") else: print("CPU request denied.") report_status(s, "Exiting") if __name__ == "__main__": main() ``` **How to Run:** 1. Save the code as `server.py` and `client.py`. 2. Run the server: `python server.py` 3. Run the client: `python client.py` (You can run multiple clients in separate terminals). **Explanation:** * **Server (server.py):** * Listens for incoming connections on a specified port. * Handles each client connection in a separate thread. * Receives JSON-encoded requests from clients. * Implements basic CPU resource management (request, release). * Responds to clients with JSON-encoded responses. * **Client (client.py):** * Connects to the server. * Sends JSON-encoded requests to the server (request CPU, report status, release CPU). * Receives JSON-encoded responses from the server. * Simulates a simple task that requests CPU, reports its status, and releases the CPU. **Improvements and Next Steps:** * **Error Handling:** Add more robust error handling to both the server and client. * **Authentication/Authorization:** Implement a proper authentication and authorization system. * **Resource Management:** Expand the resource management to include memory, disk space, and other resources. Implement a more sophisticated resource allocation algorithm. * **Task Scheduling:** Implement a task scheduler that can prioritize tasks, handle dependencies, and enforce deadlines. * **Monitoring:** Integrate monitoring tools to track system health and performance. * **Logging:** Use a logging library (e.g., `logging` in Python) to record events and errors. * **Concurrency:** Use asynchronous programming (e.g., `asyncio` in Python) for better concurrency and scalability. * **gRPC:** Migrate to gRPC for a more efficient and robust communication protocol. Define a gRPC service definition (a `.proto` file) that specifies the available RPC calls. * **Database:** Use a database to store information about users, resources, tasks, and system state. * **Distributed System:** Design the MCP as a distributed system with multiple server nodes for fault tolerance and scalability. Consider using a distributed consensus algorithm (e.g., Raft or Paxos) to ensure consistency across the nodes. **Translation to Chinese** Here's a translation of the conceptual overview and the improvements/next steps into Chinese: **概念概述 (Gài niàn ǒugài - Conceptual Overview)** MCP 本质上是一个中央控制系统。 在现代背景下，我们可以将其视为一个分布式系统，具有以下关键组件： * **服务器 (MCP 核心):** 中央权威。 它管理资源、调度任务、执行安全策略并监控整个系统健康状况。 * **客户端 (程序/进程):** 这些是与 MCP 交互以请求资源、执行任务和报告其状态的各个应用程序或进程。 * **通信协议:** 客户端和服务器交换信息的明确定义的协议。 **改进和下一步 (Gǎi jìn hé xià yī bù - Improvements and Next Steps)** * **错误处理 (Cuòwù chǔlǐ):** 向服务器和客户端添加更强大的错误处理。 * **身份验证/授权 (Shēnfèn yànzhèng/shòuquán):** 实施适当的身份验证和授权系统。 * **资源管理 (Zīyuán guǎnlǐ):** 扩展资源管理以包括内存、磁盘空间和其他资源。 实施更复杂的资源分配算法。 * **任务调度 (Rènwù diàodù):** 实施一个任务调度程序，可以优先处理任务、处理依赖关系并强制执行截止日期。 * **监控 (Jiānkòng):** 集成监控工具以跟踪系统健康状况和性能。 * **日志记录 (Rìzhì jìlù):** 使用日志记录库（例如 Python 中的 `logging`）来记录事件和错误。 * **并发 (Bìngfā):** 使用异步编程（例如 Python 中的 `asyncio`）以获得更好的并发性和可伸缩性。 * **gRPC:** 迁移到 gRPC 以获得更高效和强大的通信协议。 定义一个 gRPC 服务定义（一个 `.proto` 文件），指定可用的 RPC 调用。 * **数据库 (Shùjùkù):** 使用数据库来存储有关用户、资源、任务和系统状态的信息。 * **分布式系统 (Fēn bù shì xìtǒng):** 将 MCP 设计为具有多个服务器节点的分布式系统，以实现容错和可伸缩性。 考虑使用分布式共识算法（例如 Raft 或 Paxos）来确保节点之间的一致性。 This provides a starting point for building a system inspired by the MCP. Remember to prioritize security, scalability, and fault tolerance as you develop your implementation. Good luck!

garmin-mcp-bridge

Brings Garmin training data into Claude via Intervals.icu, enabling querying of activities, wellness, and planned workouts, as well as creating structured workouts that sync back to the Garmin watch.

ascript-mcp

Enables AI programming tools to query AScript API documentation and control real Android/iOS devices for automation scripting, including device interaction, development deployment, and file management.

video-transcriber-mcp

Enables high-performance, offline transcription of videos from 1000+ platforms and local files using whisper.cpp, with support for multiple model sizes, languages, and output formats over stdio or HTTP.

YouTube Discovery MCP

Enables discovering YouTube videos across multiple languages with personalized recommendations, feedback, and explanation of results, using YouTube Data API v3.

TypeScript MCP Server Boilerplate

A boilerplate project for quickly developing Model Context Protocol (MCP) servers using TypeScript SDK, with example implementations of tools (calculator, greeting) and resources (server info).

mcp-linkedin-post-engager-capture

Enables capturing recent posts from LinkedIn profiles and company pages, including real reaction and comment counts and publicly visible commenters, without requiring a LinkedIn account, cookies, or credentials.

Composer Trade MCP

Enables AI assistants to create, backtest, and manage automated investment strategies (symphonies) on Composer, including searching through 1000+ existing strategies, monitoring portfolio performance, and executing trades with live market data.

local-datasource

将akshare、yfinance、wbgapi、arxiv等免费公开接口封装为4个标准MCP工具，使支持MCP的智能体无需第三方账号和云额度即可直接查询金融市场、宏观经济和学术论文数据。

xhs-image-mcp

Converts text or markdown into Xiaohongshu (Little Red Book) style image cards with AI cover generation, supporting multiple themes and aspect ratios.

playwright-browser-skill

Provides 101 browser automation operations via Playwright, including navigation, interaction, content extraction, network control, and more, integrated with OpenClaw through the MCP protocol.

Zendesk MCP Server (Extended)

A production-grade MCP server for Zendesk that provides ticket management, search, user/group operations, and knowledge base access with support for local stdio and remote HTTP transports, authentication via API keys or OAuth, and multi-tenancy.

coolify-mcp

MCP server for managing Coolify instances, enabling control of applications, databases, services, servers, and more via natural language.

MaxoPerf MCP Server

Remote MCP server enabling AI agents to manage the MaxoPerf performance testing platform via natural language, including creating tests, launching cloud load runs, viewing results, and diagnosing failures.

pageindex-local-mcp

A local-first MCP server for PageIndex — the vectorless, reasoning-based RAG framework. It lets local AI agents index and query local PDF and Markdown documents through a self-hosted PageIndex installation, without requiring any PageIndex cloud API key.

GitHub MCP Server

Stateless Streamable HTTP MCP server exposing 5 GitHub tools (user info, repository metadata, file contents, issue listing, and issue creation) with token-based authentication required only for tool calls.

octo-mcp

Controls Octo Browser antidetect profiles and automates browser tasks through natural language, enabling profile lifecycle management, web scraping, and full browser automation via CDP.

mcp_searxng

MCP server for privacy-respecting web search via SearXNG, providing web, news, and image search with engine listing capabilities.

MCP Control Plane

Open-source, Windows-first control plane for operating local and homelab services through typed MCP servers, featuring 13 servers and 290 tools with infrastructure adapters for Windows, Ubuntu, Docker, and more.

immo-mcp

Analyzes French real-estate market using open data sources like DVF transactions, DPE certificates, and risk data.

Wisp AI Assistant MCP Server

Wisp AI Assistant MCP Server is a local MCP server that gives trusted AI clients read-only desktop context through Wisp: selected text, clipboard contents, active-window details, visible browser-page text, and screen snippets.

mysql-readonly-mcp

A readonly MCP server for MySQL databases that ensures safety by whitelisting only SELECT, SHOW, DESCRIBE, and EXPLAIN statements, with table blacklist support and configurable limits.

LinkedIn MCP Server

Enables natural language interaction with LinkedIn, including profile retrieval, job searching, messaging, and post engagement.

ansim-gil-dongmu

An MCP server that reviews routes for elderly and disabled pedestrians, checking station accessibility and providing safer alternatives.

kingdee_star

Enables read-only access to Kingdee Cloud Star financial data, invoice tax calculation, and generation of reimbursement draft payloads through MCP tools.

PDF Knowledgebase MCP Server

A Model Context Protocol server that enables intelligent document search and retrieval from PDF collections, providing semantic search capabilities powered by OpenAI embeddings and ChromaDB vector storage.