Discover Awesome MCP Servers

@vibetools/dokploy-mcp

Enables deploying, managing, and interacting with Dokploy applications via natural language using three core MCP tools: search, execute, and list_profiles.

Python MCP Sandbox

An interactive Python code execution environment that allows users and LLMs to safely execute Python code and install packages in isolated Docker containers.

StackScan MCP Server

Enables natural language queries about the technologies behind websites and the companies running them, using the StackScan Tech Lookup API. Includes tools for domain technology lookup, company details, technology usage stats, and batch company lookups.

gangtise-mcp

This MCP server enables AI assistants to access Gangtise investment research platform data, including market data, financial reports, research opinions, and more via the Gangtise OpenAPI.

twitter-username-changes-mcp

Tracks historical changes of Twitter usernames to detect frequent screen name changes, which can be a red flag for potential scam risks.

mcp-devbridge

Exposes a local development project as a secure MCP server over a stable HTTPS URL via Cloudflare tunnel, with configurable access permissions and 33 development tools for file, command, Git, and process operations.

Unified Search MCP Server

Enables AI agents to perform multi-platform searches (Douyin, Xiaohongshu, Zhihu, CSDN) and reverse-engineer arbitrary websites using browser automation and CDP.

micro-mcp

Enables building, verifying, previewing, deploying, diagnosing, updating, and rolling back full-stack Micro apps through typed local MCP tools backed by micro-cli.

Remote MCP Server Authless

Deploy a remote MCP server on Cloudflare Workers without authentication, enabling tools to be used from Cloudflare AI Playground or Claude Desktop via SSE.

LinkedIn Sales Navigator No Cookies Required MCP Server

Provides access to the LinkedIn Sales Navigator API without requiring browser cookies for authentication. It enables AI assistants to interact with sales data and various utility endpoints including TV Maze and deck of cards.

PersonalizationMCP

Enables AI assistants to access and interact with personal data from platforms like Steam, YouTube, Bilibili, Spotify, and Reddit for personalized, context-aware interactions.

LinkedIn Ad Library MCP Server

Enables AI assistants to search LinkedIn's Ad Library for sponsored ads, job postings, and paid endorsements, providing insights into competitor strategies and hiring signals.

shadcndashboard-mcp

MCP server for Shadcn Dashboard that enables AI to discover, search, and install UI blocks directly into projects without copy-paste.

fpl-mcp

An MCP server that equips AI assistants with 33 tools for Fantasy Premier League analytics, including fixtures, squad management, live scores, transfers, captaincy, and price movements, using async access to official FPL endpoints with TTL caching.

gworkspace-mcp

A production-ready MCP server that gives AI assistants full, authenticated access to the entire Google Workspace ecosystem.

chatmux

Local-first MCP server that puts your own LINE and Telegram chats behind one data layer — a daemon on your machine logs in with your own account, stores messages to JSONL + SQLite/FTS5, and exposes 6 MCP tools over streamable HTTP on 127.0.0.1. Not a hosted service and not an npx one-liner: you clone the repo, run the daemon, and log in yourself.

MCP Bridge

一个动态MCP服务桥接器，支持通过Web UI或API动态注册MCP能力，实现MCP Client与前端之间的双向通信。

thread-storm

MCP server for publishing threads to Threads and Twitter/X simultaneously from Claude Code. Journal your thoughts, convert them to viral threads, and post everywhere at once.

My Awesome MCP

A basic MCP server built with FastMCP framework that provides example tools including message echoing and server information retrieval. Supports both stdio and HTTP transports with Docker deployment capabilities.

Kintone Book Management MCP Tool

A Model Context Protocol (MCP) server that provides a tool for retrieving and managing book information from a Kintone database application.

my-mcp-server

A TypeScript MCP server boilerplate with Streamable HTTP support, providing tools for greeting, calculation, time, geocoding, weather, and image generation, plus a server-info resource and code-review prompt.

EDINET DB MCP Server

Structured financial data for ~3,800 Japanese listed companies from EDINET regulatory filings — financials, major shareholders, segments, executive compensation, and corporate history. Remote MCP over HTTPS with OAuth 2.0, free tier.

NIH Research MCP Demo

Enables interaction with synthetic NIH-style clinical research data through tools for searching publications, querying patient metadata, analyzing AAA measurements, and retrieving protocol guidance.

MCP Server Implementation Guide

以下是一个指南和实现，用于创建你自己的 MCP (模型控制协议) 服务器，以便与 Cursor 集成： **标题：创建你自己的 Cursor 集成 MCP 服务器指南与实现** **简介：** Cursor 是一款强大的代码编辑器，它允许通过 MCP (Model Control Protocol) 与外部语言模型进行交互。 本指南将引导你完成创建自己的 MCP 服务器的过程，以便将你自己的语言模型集成到 Cursor 中。 **1. 了解 MCP (Model Control Protocol):** * **目的：** MCP 是一种允许 Cursor 与外部语言模型进行通信的协议。 它定义了 Cursor 如何向模型发送请求以及模型如何返回响应。 * **通信方式：** MCP 通常使用 JSON over WebSocket 进行通信。 * **关键消息类型：** * **`completion` 请求：** Cursor 向模型发送代码补全请求。 * **`completion` 响应：** 模型返回代码补全建议。 * **`chat` 请求：** Cursor 向模型发送聊天请求。 * **`chat` 响应：** 模型返回聊天回复。 * **`edit` 请求：** Cursor 向模型发送代码编辑请求。 * **`edit` 响应：** 模型返回代码编辑建议。 * **`health` 请求：** Cursor 向服务器发送健康检查请求。 * **`health` 响应：** 服务器返回健康状态。 **2. 选择编程语言和框架：** 你可以使用任何你喜欢的编程语言和框架来构建 MCP 服务器。 一些常见的选择包括： * **Python:** 使用 `websockets` 或 `aiohttp` 库。 * **Node.js:** 使用 `ws` 或 `socket.io` 库。 * **Go:** 使用 `gorilla/websocket` 库。 本指南将使用 Python 和 `websockets` 库作为示例。 **3. 设置 WebSocket 服务器：** 首先，你需要设置一个 WebSocket 服务器来监听来自 Cursor 的连接。 ```python import asyncio import websockets import json async def handle_connection(websocket, path): print(f"New connection from {websocket.remote_address}") try: async for message in websocket: print(f"Received message: {message}") try: data = json.loads(message) # 处理消息 response = await process_message(data) await websocket.send(json.dumps(response)) except json.JSONDecodeError: print("Invalid JSON received") await websocket.send(json.dumps({"error": "Invalid JSON"})) except Exception as e: print(f"Error processing message: {e}") await websocket.send(json.dumps({"error": str(e)})) except websockets.exceptions.ConnectionClosedError: print(f"Connection closed unexpectedly from {websocket.remote_address}") except websockets.exceptions.ConnectionClosedOK: print(f"Connection closed normally from {websocket.remote_address}") finally: print(f"Connection closed from {websocket.remote_address}") async def process_message(data): # 在这里处理不同类型的 MCP 请求 if data.get("type") == "completion": return await handle_completion(data) elif data.get("type") == "chat": return await handle_chat(data) elif data.get("type") == "edit": return await handle_edit(data) elif data.get("type") == "health": return await handle_health(data) else: return {"error": "Unknown message type"} async def handle_completion(data): # TODO: 调用你的语言模型进行代码补全 prompt = data.get("prompt") # 示例：返回一个简单的补全建议 completion = f"// This is a completion for: {prompt}" return {"completion": completion} async def handle_chat(data): # TODO: 调用你的语言模型进行聊天 message = data.get("message") # 示例：返回一个简单的聊天回复 response = f"You said: {message}" return {"response": response} async def handle_edit(data): # TODO: 调用你的语言模型进行代码编辑 code = data.get("code") instruction = data.get("instruction") # 示例：返回一个简单的编辑建议 edited_code = f"// Edited code based on: {instruction}\n{code}" return {"edited_code": edited_code} async def handle_health(data): # 返回服务器的健康状态 return {"status": "ok"} async def main(): async with websockets.serve(handle_connection, "localhost", 8765): print("WebSocket server started at ws://localhost:8765") await asyncio.Future() # 保持服务器运行 if __name__ == "__main__": asyncio.run(main()) ``` **4. 处理 MCP 请求：** 在 `process_message` 函数中，你需要根据 `data.get("type")` 的值来处理不同类型的 MCP 请求。 * **`completion` 请求：** * 从 `data` 中提取代码补全所需的上下文信息（例如，当前代码、光标位置等）。 * 调用你的语言模型来生成代码补全建议。 * 将补全建议封装在 `completion` 响应中并返回。 * **`chat` 请求：** * 从 `data` 中提取聊天消息。 * 调用你的语言模型来生成聊天回复。 * 将回复封装在 `chat` 响应中并返回。 * **`edit` 请求：** * 从 `data` 中提取代码和编辑指令。 * 调用你的语言模型来生成代码编辑建议。 * 将编辑后的代码封装在 `edit` 响应中并返回。 * **`health` 请求：** * 返回服务器的健康状态。 **5. 集成你的语言模型：** 在 `handle_completion`、`handle_chat` 和 `handle_edit` 函数中，你需要集成你自己的语言模型。 这可能涉及： * 加载你的语言模型。 * 预处理输入数据。 * 调用语言模型进行推理。 * 后处理输出数据。 **6. 配置 Cursor：** 1. 打开 Cursor 的设置。 2. 搜索 "Model Control Protocol"。 3. 启用 "Enable Model Control Protocol"。 4. 在 "Model Control Protocol URL" 中输入你的 MCP 服务器的 URL (例如，`ws://localhost:8765`)。 **7. 测试：** 1. 运行你的 MCP 服务器。 2. 在 Cursor 中打开一个代码文件。 3. 尝试代码补全、聊天或代码编辑功能。 4. 检查你的 MCP 服务器是否收到请求并返回了正确的响应。 **8. 错误处理：** * 在服务器端，捕获所有可能的异常并返回包含错误信息的 JSON 响应。 * 在 Cursor 端，检查响应中是否包含错误信息并向用户显示。 **9. 优化：** * **性能：** 优化你的语言模型和 MCP 服务器以提高性能。 * **可扩展性：** 设计你的 MCP 服务器以支持多个并发连接。 * **安全性：** 考虑安全性问题，例如身份验证和授权。 **示例 JSON 消息格式：** **Completion Request:** ```json { "type": "completion", "prompt": "def hello_world():\n " } ``` **Completion Response:** ```json { "completion": "print('Hello, world!')" } ``` **Chat Request:** ```json { "type": "chat", "message": "How do I write a for loop in Python?" } ``` **Chat Response:** ```json { "response": "You can write a for loop in Python like this: `for i in range(10): print(i)`" } ``` **Edit Request:** ```json { "type": "edit", "code": "def add(a, b):\n return a + b", "instruction": "Add a docstring to the function." } ``` **Edit Response:** ```json { "edited_code": "def add(a, b):\n \"\"\"Adds two numbers together.\"\"\"\n return a + b" } ``` **Health Request:** ```json { "type": "health" } ``` **Health Response:** ```json { "status": "ok" } ``` **总结：** 通过遵循本指南，你可以创建自己的 MCP 服务器，并将你自己的语言模型集成到 Cursor 中。 这将使你能够利用你自己的模型来增强 Cursor 的代码补全、聊天和代码编辑功能。 记住，这只是一个起点，你需要根据你的具体需求进行调整和优化。 **重要提示：** * 确保你的语言模型符合 Cursor 的使用条款和隐私政策。 * 仔细测试你的 MCP 服务器，以确保其稳定性和可靠性。 * 考虑安全性问题，例如身份验证和授权。 This translation provides a comprehensive guide and implementation example for creating your own MCP server for Cursor integration. It covers the key concepts, steps, and considerations involved in the process. Remember to replace the placeholder comments with your actual language model integration logic. Good luck!

ps-mcp

An MCP server for PowerSchool plugin developers, providing read/write access to plugin workspaces, scaffolding, validation, packaging, and access to the PS data model, tag reference, and documentation.

h3c-hcl-mcp

Enables AI agents to interact with local H3C Cloud Lab network simulation environments, allowing discovery of projects, devices, and links, as well as execution of Comware CLI commands.

tick-mcp

Enables AI assistants to manage TickTick tasks, projects, habits, tags, and focus stats using 71 tools via the Model Context Protocol.

Microsoft Paint MCP Server

MCP server that automates Microsoft Paint on Windows, offering tools to draw freehand strokes, polylines, and logarithmic spirals through Win32 API calls.

scanner-mcp

Enables scanning documents via network (eSCL) or USB (WIA/SANE) scanners, returning inline images, OCR text, or saved PDFs for Claude to read.

MCPing

Enables AI assistants to send desktop notifications on macOS with rich formatting, urgency levels, and sound options.