Discover Awesome MCP Servers

bookmarks-mcp

A local, read-only MCP server that makes bookmarks from browsers, read-later apps, and export files accessible to AI assistants, enabling search, triage, and summarization without sending data anywhere.

Manas

Enables interaction with a local knowledge repository via MCP, providing tools for capturing, indexing, and searching Markdown notes with Git version control and optional semantic search.

Self-Assembling Forensic MCP Server

Enables AI assistants to perform digital forensics analysis including memory analysis, file metadata inspection, and threat-intelligence lookups.

Bitso MCP Server

Enables interaction with the Bitso cryptocurrency exchange API to access withdrawals and fundings data. Provides comprehensive tools for listing, filtering, and retrieving withdrawal and funding transactions with proper authentication and error handling.

MCP Code Assistant

Provides file operations (read/write) with an extensible architecture designed for future C code compilation and executable execution capabilities.

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.

rustchain-mcp

Enables querying and interacting with the RustChain blockchain from Claude Code or any MCP-compatible client, including balance checks, miners, epoch info, health, and transfers.

AskTheApi Team Builder

基于AutoGen的、用于与 OpenAPI API 通信的代理网络构建器。

IRIS Legacy

Archived monolithic MCP server that provided 28 tools for Microsoft 365 (email, calendar, Teams, users, files), Italian PEC certified email, booking, and document management. Replaced by 8 atomic MCP servers.

Sync Socials Agent

A hosted MCP server that lets AI agents generate content, upload media, create drafts, schedule posts, and publish to TikTok, Instagram, Facebook, and YouTube.

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.

Unified Diff MCP Server

Transforms code diffs into beautiful visual comparisons with support for GitHub Gist sharing and local file output (HTML/PNG).

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.

SAP MCP Server

Enables AI assistants to securely connect with SAP ABAP and BTP services, allowing execution of function modules, BAPIs, table reads, and various BTP operations through MCP.

MAST

Code-search engine that parses TypeScript and JavaScript with a real AST, stores the symbol graph in SQLite, and serves AI assistants exact matching declarations (functions, classes, types) via BM25 fused with declaration-exact ranking — saving tokens by returning only the relevant code instead of whole files.

Feishu Bitable MCP Server

Enables interacting with Feishu (Lark) multidimensional tables through MCP tools. Supports listing tables, reading records, searching records and apps, and getting views.

MCPHubs

A unified gateway and web dashboard that aggregates multiple MCP servers into a single Streamable HTTP endpoint. It supports stdio, SSE, and HTTP protocols, featuring optimized tool exposure modes to reduce token consumption for AI clients.

Nano Banana

Generate, edit, and restore images using natural language prompts through the Gemini 2.5 Flash image model. Supports creating app icons, seamless patterns, visual stories, and technical diagrams with smart file management.

tibet-voice-cache-mcp

Enables MCP-compatible AI clients to store and recall voice conversation context by caching user and AI utterances, supporting formatted context summaries for multi-turn voice interactions.

marketifyall-mcp

A thin bridge that connects any stdio-based MCP client to the MarketifyALL workspace, enabling content generation, search, and discovery through a hosted MCP server with graduated safety controls.

mabl MCP Server

Managed MCP server that enables AI assistants to run, analyze, and create mabl tests, and query results across workspaces.

pyNastran MCP Server

An MCP server that enables AI agents to interact with Nastran FEA models by reading, writing, and analyzing BDF and OP2 files. It provides tools for mesh quality assessment, geometric analysis, and automated report generation for structural engineering workflows.

sevdesk-mcp

Integrates with the sevdesk German accounting API, providing 76 tools for full CRUD operations across contacts, invoices, vouchers, orders, credit notes, bank accounts, transactions, parts, tags, addresses, and communication ways.

axie-mcp

Provides comprehensive access to Axie Infinity data, including detailed Axie stats, marketplace listings, land information, and player leaderboards. It allows users to query real-time game info and market statistics through natural language.

lms-ssh-client-mcp

Enables LLMs to securely SSH into remote servers, execute commands, and manage files via SFTP including listing, reading, writing, deleting, and renaming files.

predictfun-mcp

MCP (Model Context Protocol) server that gives AI agents structured access to Predict.fun — a prediction market protocol on BNB Chain with $1.5B+ volume and yield-bearing mechanics via Venus Protocol. Indexes data from three subgraphs: orderbook activity, position lifecycle, and yield mechanics.

pgwarden-mcp

A Postgres MCP server that enables AI agents to safely access production databases through deny-by-default YAML policies, PII masking, row limits, and required predicates. It also provides DBA capabilities like index tuning, health checks, and EXPLAIN plans, with support for multiple databases.

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!