发现优秀的 MCP 服务器

Eyevinn Open Source Cloud MCP Server

镜子 (jìng zi)

Document Tools

Enables document conversion and processing through an MCP server interface for AI assistants.

kilo-dev-mcp-server

An MCP server for internationalization (i18n) tasks, providing tools to translate, move, list, and remove translation keys in JSON files for the Kilo Code extension.

XRootD MCP Server

An MCP server providing access to XRootD file systems, allowing LLMs to browse directories, read file metadata, and access contents via the root:// protocol. It supports advanced features like campaign discovery, file searching, and ROOT file analysis for scientific data management.

VideoGen Advisor

A unified MCP server for video generation that intelligently routes requests to HeyGen (for avatar/presenter videos) or Google Veo (for creative/cinematic content).

jsx-notation

Compresses React/Next.js files, HTML, and SVG into a compact JSXN notation optimized for AI assistants, reducing token consumption by ~40%. Provides an MCP server with tools to read, encode, and decode JSXN.

Grok SearXNG Adapter MCP

MCP server that enables web search via Grok's web_search tool and web page fetching via Firecrawl, with configurable options for search mode and content extraction.

Cloudflare Playwright MCP

Enables AI assistants to perform web automation tasks such as navigation, typing, clicking, and taking screenshots using Playwright on Cloudflare Workers. This server allows LLMs to interact with and control a browser across platforms like Claude Desktop and GitHub Copilot.

@webability/mcp

An MCP server for web accessibility testing that enables scanning, auditing, and fixing WCAG, ADA, and other compliance issues directly from your IDE, with free local scans, AI-generated framework-aware fixes, and verification capabilities.

apocrypha

Enables assistants to maintain an append-only memory for personal context that should not be stored in their built-in memory, with tools for noting, recalling, and compressing memories.

vision-mcp-ms

A cloud vision MCP server that enables text-only LLMs like DeepSeek to analyze images via OpenAI-compatible APIs (e.g., SiliconFlow), offering a single analyze_image tool with support for URL/data URLs and automatic model fallback.

mcp-gateway

Enables multiple MCP clients to securely access various third-party MCP backends through a single HTTP gateway, with modern MCP handshake compatibility and session lifecycle management.

MCP Docs Server

Aggregates documentation from multiple sources (llms.txt format or web scraping) and provides semantic search capabilities using vector embeddings and hybrid search for each documentation source.

Google Workspace MCP Server

Enables AI agents to send emails and append content to Google Docs using Gmail and Google Docs APIs.

docs-cache-mcp

A local MCP server that fetches official library documentation (llms.txt-first), caches it to disk, and serves relevant sections to coding agents offline with deterministic retrieval.

Getting Started with Create React App

用于安全上限和 MCP 服务器的 AI 原生代理

quantum-demos-mcp

MCP server for authoring Quantum customer demos from Claude Code; enables listing, viewing, and updating demos with a guide and prompts.

SSB-MCP

用于与挪威统计局 (SSB) API 交互的 MCP 服务器 - 使 AI 代理能够访问挪威统计数据

Medicare MCP Server

Provides comprehensive access to CMS Medicare data including physician services, prescriber information, hospital quality metrics, drug spending, formulary coverage, and ASP pricing for healthcare analysis and decision-making.

Playwright MCP

A server that provides browser automation capabilities using Playwright, enabling LLMs to interact with web pages through structured accessibility snapshots without requiring screenshots or vision models.

@shipeasy/mcp

A unified MCP server for experimentation and i18n, enabling AI assistants to manage feature flags, experiments, metrics, events, and string translations via natural language.

Wave MCP Server

MCP server for Wave accounting that provides tools for managing chart of accounts, invoices, customers, vendors, products, and reports via the Wave GraphQL API.

mysql-mcp-server-all

A read-only MySQL MCP server supporting stdio, SSE, and Streamable HTTP transports, enabling secure querying and schema inspection of MySQL databases from MCP clients.

directa-mcp

MCP server that connects Claude to Directa's Darwin trading API, enabling local account management, portfolio views, and order operations through natural language.

Brosh Browser Screenshot

Captures comprehensive webpage screenshots with intelligent scrolling, text extraction, and HTML analysis, enabling AI tools to visually inspect and understand web content through the Model Context Protocol.

flutterclimcp

好的，这是一个使用 Flutter CLI 和 MCP (Model Context Protocol) 服务器创建 Flutter 项目的有趣示例项目： **项目名称：** 猜数字游戏 (Guess the Number Game) **项目描述：** 这是一个简单的猜数字游戏，用户需要猜一个由计算机随机生成的数字。游戏会提供反馈，告诉用户猜的数字是太高还是太低，直到用户猜对为止。我们将使用 MCP 服务器来处理游戏逻辑，而 Flutter 应用将负责用户界面和与服务器的通信。 **技术栈：** * **Flutter:** 用于构建用户界面。 * **Flutter CLI:** 用于创建和管理 Flutter 项目。 * **MCP Server (Python):** 用于处理游戏逻辑，例如生成随机数、比较猜测和提供反馈。 * **HTTP:** 用于 Flutter 应用和 MCP 服务器之间的通信。 **步骤：** 1. **设置 MCP 服务器 (Python):** ```python from http.server import BaseHTTPRequestHandler, HTTPServer import json import random class RequestHandler(BaseHTTPRequestHandler): def do_POST(self): if self.path == '/guess': content_length = int(self.headers['Content-Length']) post_data = self.rfile.read(content_length) data = json.loads(post_data.decode('utf-8')) guess = data.get('guess') if not hasattr(self.server, 'secret_number'): self.server.secret_number = random.randint(1, 100) self.server.num_guesses = 0 self.server.num_guesses += 1 if guess is None: response = {'error': 'Missing guess parameter'} elif guess < self.server.secret_number: response = {'result': 'too_low'} elif guess > self.server.secret_number: response = {'result': 'too_high'} else: response = {'result': 'correct', 'guesses': self.server.num_guesses} del self.server.secret_number # Reset for next game del self.server.num_guesses self.send_response(200) self.send_header('Content-type', 'application/json') self.end_headers() self.wfile.write(json.dumps(response).encode('utf-8')) else: self.send_response(404) self.end_headers() def run(server_class=HTTPServer, handler_class=RequestHandler, port=8000): server_address = ('', port) httpd = server_class(server_address, handler_class) print(f'Starting server on port {port}') httpd.serve_forever() if __name__ == '__main__': run() ``` * 将此代码保存为 `mcp_server.py`。 * 运行服务器：`python mcp_server.py` 2. **创建 Flutter 项目:** ```bash flutter create guess_the_number cd guess_the_number ``` 3. **修改 `lib/main.dart`:** ```dart import 'package:flutter/material.dart'; import 'package:http/http.dart' as http; import 'dart:convert'; void main() { runApp(MyApp()); } class MyApp extends StatelessWidget { @override Widget build(BuildContext context) { return MaterialApp( title: 'Guess the Number', theme: ThemeData( primarySwatch: Colors.blue, ), home: GuessTheNumberPage(), ); } } class GuessTheNumberPage extends StatefulWidget { @override _GuessTheNumberPageState createState() => _GuessTheNumberPageState(); } class _GuessTheNumberPageState extends State<GuessTheNumberPage> { final TextEditingController _guessController = TextEditingController(); String _message = ''; bool _gameOver = false; Future<void> _checkGuess(String guess) async { try { final int? parsedGuess = int.tryParse(guess); if (parsedGuess == null) { setState(() { _message = 'Please enter a valid number.'; }); return; } final response = await http.post( Uri.parse('http://localhost:8000/guess'), // Replace with your server address headers: {'Content-Type': 'application/json'}, body: jsonEncode({'guess': parsedGuess}), ); if (response.statusCode == 200) { final data = jsonDecode(response.body); final result = data['result']; setState(() { if (result == 'too_low') { _message = 'Too low! Try again.'; } else if (result == 'too_high') { _message = 'Too high! Try again.'; } else if (result == 'correct') { _message = 'Congratulations! You guessed the number in ${data['guesses']} tries.'; _gameOver = true; } else if (data.containsKey('error')) { _message = 'Error: ${data['error']}'; } else { _message = 'Unexpected response from server.'; } }); } else { setState(() { _message = 'Failed to connect to the server. Status code: ${response.statusCode}'; }); } } catch (e) { setState(() { _message = 'An error occurred: $e'; }); } } void _resetGame() { setState(() { _message = ''; _guessController.clear(); _gameOver = false; }); } @override Widget build(BuildContext context) { return Scaffold( appBar: AppBar( title: Text('Guess the Number'), ), body: Padding( padding: const EdgeInsets.all(16.0), child: Column( mainAxisAlignment: MainAxisAlignment.center, children: <Widget>[ Text( 'I\'m thinking of a number between 1 and 100.', style: TextStyle(fontSize: 16), ), SizedBox(height: 20), TextField( controller: _guessController, keyboardType: TextInputType.number, decoration: InputDecoration( labelText: 'Enter your guess', border: OutlineInputBorder(), ), enabled: !_gameOver, ), SizedBox(height: 20), ElevatedButton( onPressed: _gameOver ? null : () { _checkGuess(_guessController.text); }, child: Text('Guess'), ), SizedBox(height: 20), Text( _message, style: TextStyle(fontSize: 18, fontWeight: FontWeight.bold), textAlign: TextAlign.center, ), if (_gameOver) ElevatedButton( onPressed: _resetGame, child: Text('Play Again'), ), ], ), ), ); } } ``` 4. **添加 `http` 依赖:** ```bash flutter pub add http ``` 5. **运行 Flutter 应用:** ```bash flutter run ``` **解释:** * **MCP 服务器 (Python):** * 监听端口 8000 上的 HTTP POST 请求。 * 当收到 `/guess` 请求时，它会解析 JSON 数据，提取用户的猜测。 * 如果这是第一次猜测，它会生成一个 1 到 100 之间的随机数。 * 它会将用户的猜测与秘密数字进行比较，并返回 `too_low`、`too_high` 或 `correct`。 * 如果猜测正确，它会返回猜测次数并重置游戏。 * **Flutter 应用:** * 包含一个文本字段，用户可以在其中输入他们的猜测。 * 包含一个按钮，用户可以点击该按钮来提交他们的猜测。 * 使用 `http` 包向 MCP 服务器发送 POST 请求，并将用户的猜测作为 JSON 数据发送。 * 解析服务器的响应，并更新 UI 以显示反馈（太高、太低、正确）。 * 如果用户猜对了，它会显示一条祝贺消息和猜测次数。 * 包含一个“再玩一次”按钮，可以重置游戏。 **如何运行:** 1. 首先，运行 `mcp_server.py`。 2. 然后，运行 Flutter 应用。 3. 在 Flutter 应用中，输入你的猜测并点击“猜测”按钮。 4. 查看应用中的反馈，并继续猜测直到你猜对为止。 **改进:** * **错误处理:** 添加更健壮的错误处理，例如处理服务器连接错误。 * **UI 改进:** 改进 UI 以使其更具吸引力。 * **难度级别:** 添加难度级别，允许用户选择数字范围。 * **历史记录:** 显示用户的猜测历史记录。 * **使用更复杂的 MCP 协议:** 虽然这个例子使用简单的 HTTP，但你可以探索更复杂的 MCP 协议，例如 gRPC 或 Thrift，以获得更好的性能和类型安全。 这个示例展示了如何使用 Flutter CLI 和 MCP 服务器创建一个简单的 Flutter 项目。 你可以根据自己的需要修改和扩展此项目。 关键在于将 UI 逻辑与业务逻辑分离，并使用 MCP 服务器来处理业务逻辑。 **中文翻译：** 好的，这是一个使用 Flutter CLI 和 MCP (模型上下文协议) 服务器创建一个 Flutter 项目的有趣示例项目： **项目名称：** 猜数字游戏 (Guess the Number Game) **项目描述：** 这是一个简单的猜数字游戏，用户需要猜一个由计算机随机生成的数字。游戏会提供反馈，告诉用户猜的数字是太高还是太低，直到用户猜对为止。我们将使用 MCP 服务器来处理游戏逻辑，而 Flutter 应用将负责用户界面和与服务器的通信。 **技术栈：** * **Flutter:** 用于构建用户界面。 * **Flutter CLI:** 用于创建和管理 Flutter 项目。 * **MCP Server (Python):** 用于处理游戏逻辑，例如生成随机数、比较猜测和提供反馈。 * **HTTP:** 用于 Flutter 应用和 MCP 服务器之间的通信。 **步骤：** 1. **设置 MCP 服务器 (Python):** ```python from http.server import BaseHTTPRequestHandler, HTTPServer import json import random class RequestHandler(BaseHTTPRequestHandler): def do_POST(self): if self.path == '/guess': content_length = int(self.headers['Content-Length']) post_data = self.rfile.read(content_length) data = json.loads(post_data.decode('utf-8')) guess = data.get('guess') if not hasattr(self.server, 'secret_number'): self.server.secret_number = random.randint(1, 100) self.server.num_guesses = 0 self.server.num_guesses += 1 if guess is None: response = {'error': 'Missing guess parameter'} elif guess < self.server.secret_number: response = {'result': 'too_low'} elif guess > self.server.secret_number: response = {'result': 'too_high'} else: response = {'result': 'correct', 'guesses': self.server.num_guesses} del self.server.secret_number # Reset for next game del self.server.num_guesses self.send_response(200) self.send_header('Content-type', 'application/json') self.end_headers() self.wfile.write(json.dumps(response).encode('utf-8')) else: self.send_response(404) self.end_headers() def run(server_class=HTTPServer, handler_class=RequestHandler, port=8000): server_address = ('', port) httpd = server_class(server_address, handler_class) print(f'Starting server on port {port}') httpd.serve_forever() if __name__ == '__main__': run() ``` * 将此代码保存为 `mcp_server.py`。 * 运行服务器：`python mcp_server.py` 2. **创建 Flutter 项目:** ```bash flutter create guess_the_number cd guess_the_number ``` 3. **修改 `lib/main.dart`:** ```dart import 'package:flutter/material.dart'; import 'package:http/http.dart' as http; import 'dart:convert'; void main() { runApp(MyApp()); } class MyApp extends StatelessWidget { @override Widget build(BuildContext context) { return MaterialApp( title: 'Guess the Number', theme: ThemeData( primarySwatch: Colors.blue, ), home: GuessTheNumberPage(), ); } } class GuessTheNumberPage extends StatefulWidget { @override _GuessTheNumberPageState createState() => _GuessTheNumberPageState(); } class _GuessTheNumberPageState extends State<GuessTheNumberPage> { final TextEditingController _guessController = TextEditingController(); String _message = ''; bool _gameOver = false; Future<void> _checkGuess(String guess) async { try { final int? parsedGuess = int.tryParse(guess); if (parsedGuess == null) { setState(() { _message = 'Please enter a valid number.'; }); return; } final response = await http.post( Uri.parse('http://localhost:8000/guess'), // Replace with your server address headers: {'Content-Type': 'application/json'}, body: jsonEncode({'guess': parsedGuess}), ); if (response.statusCode == 200) { final data = jsonDecode(response.body); final result = data['result']; setState(() { if (result == 'too_low') { _message = 'Too low! Try again.'; } else if (result == 'too_high') { _message = 'Too high! Try again.'; } else if (result == 'correct') { _message = 'Congratulations! You guessed the number in ${data['guesses']} tries.'; _gameOver = true; } else if (data.containsKey('error')) { _message = 'Error: ${data['error']}'; } else { _message = 'Unexpected response from server.'; } }); } else { setState(() { _message = 'Failed to connect to the server. Status code: ${response.statusCode}'; }); } } catch (e) { setState(() { _message = 'An error occurred: $e'; }); } } void _resetGame() { setState(() { _message = ''; _guessController.clear(); _gameOver = false; }); } @override Widget build(BuildContext context) { return Scaffold( appBar: AppBar( title: Text('Guess the Number'), ), body: Padding( padding: const EdgeInsets.all(16.0), child: Column( mainAxisAlignment: MainAxisAlignment.center, children: <Widget>[ Text( 'I\'m thinking of a number between 1 and 100.', style: TextStyle(fontSize: 16), ), SizedBox(height: 20), TextField( controller: _guessController, keyboardType: TextInputType.number, decoration: InputDecoration( labelText: 'Enter your guess', border: OutlineInputBorder(), ), enabled: !_gameOver, ), SizedBox(height: 20), ElevatedButton( onPressed: _gameOver ? null : () { _checkGuess(_guessController.text); }, child: Text('Guess'), ), SizedBox(height: 20), Text( _message, style: TextStyle(fontSize: 18, fontWeight: FontWeight.bold), textAlign: TextAlign.center, ), if (_gameOver) ElevatedButton( onPressed: _resetGame, child: Text('Play Again'), ), ], ), ), ); } } ``` 4. **添加 `http` 依赖:** ```bash flutter pub add http ``` 5. **运行 Flutter 应用:** ```bash flutter run ``` **解释:** * **MCP 服务器 (Python):** * 监听端口 8000 上的 HTTP POST 请求。 * 当收到 `/guess` 请求时，它会解析 JSON 数据，提取用户的猜测。 * 如果这是第一次猜测，它会生成一个 1 到 100 之间的随机数。 * 它会将用户的猜测与秘密数字进行比较，并返回 `too_low`、`too_high` 或 `correct`。 * 如果猜测正确，它会返回猜测次数并重置游戏。 * **Flutter 应用:** * 包含一个文本字段，用户可以在其中输入他们的猜测。 * 包含一个按钮，用户可以点击该按钮来提交他们的猜测。 * 使用 `http` 包向 MCP 服务器发送 POST 请求，并将用户的猜测作为 JSON 数据发送。 * 解析服务器的响应，并更新 UI 以显示反馈（太高、太低、正确）。 * 如果用户猜对了，它会显示一条祝贺消息和猜测次数。 * 包含一个“再玩一次”按钮，可以重置游戏。 **如何运行:** 1. 首先，运行 `mcp_server.py`。 2. 然后，运行 Flutter 应用。 3. 在 Flutter 应用中，输入你的猜测并点击“猜测”按钮。 4. 查看应用中的反馈，并继续猜测直到你猜对为止。 **改进:** * **错误处理:** 添加更健壮的错误处理，例如处理服务器连接错误。 * **UI 改进:** 改进 UI 以使其更具吸引力。 * **难度级别:** 添加难度级别，允许用户选择数字范围。 * **历史记录:** 显示用户的猜测历史记录。 * **使用更复杂的 MCP 协议:** 虽然这个例子使用简单的 HTTP，但你可以探索更复杂的 MCP 协议，例如 gRPC 或 Thrift，以获得更好的性能和类型安全。 这个示例展示了如何使用 Flutter CLI 和 MCP 服务器创建一个简单的 Flutter 项目。 你可以根据自己的需要修改和扩展此项目。 关键在于将 UI 逻辑与业务逻辑分离，并使用 MCP 服务器来处理业务逻辑。 This provides a complete, runnable example with explanations and improvements. Remember to replace `http://localhost:8000/guess` with the actual address of your MCP server if it's running on a different machine or port. Good luck!

Terragrunt MCP Server

Enables analysis and validation of Terragrunt projects, including dependency mapping, stack structure, and optimization suggestions, directly in IDEs that support MCP.

logseq-api-mcp

AI assistant integration with Logseq knowledge graph: 21 tools to read, write, query, and search notes, enabling seamless interaction with your notes.

graphql-agent-toolkit

Enables AI agents to interact with any GraphQL API by introspecting the schema and exposing queries and mutations as MCP tools, with built-in pagination, semantic search, and framework adapters.

DALL-E MCP Server

Enables AI assistants to generate high-quality images using OpenAI's DALL-E 3 model with configurable parameters like size, quality, and style. Generated images are automatically saved to the local filesystem with comprehensive error handling.