发现优秀的 MCP 服务器

MCP-Odoo

A bridge that allows AI agents to access and manipulate Odoo ERP data through a standardized Model Context Protocol interface, supporting partner information, accounting data, financial records reconciliation, and invoice queries.

MCP Demo Server

A minimal fastmcp demonstration server that provides a simple addition tool through the MCP protocol, supporting deployment via Docker with multiple transport modes.

MCP Client-Server Sandbox for LLM Augmentation

用于增强 LLM 推理（本地或云端）的完整沙盒，集成了 MCP 客户端-服务器。为 MCP 服务器验证和 Agentic 评估提供低摩擦的试验平台。

Confluence MCP Server

Enables integration with Atlassian Confluence to browse spaces, search content using CQL, and manage pages directly from MCP-compatible applications. It automatically converts Confluence storage formats into markdown for seamless interaction with AI-driven editors and tools.

Somnia MCP Server

Enables AI agents to interact with the Somnia blockchain network, including documentation search, blockchain queries, wallet management, cryptographic signing, and on-chain operations.

Applitools MCP Server

Enables AI assistants to set up, manage, and analyze visual tests using Applitools Eyes within Playwright JavaScript and TypeScript projects. It supports adding visual checkpoints, configuring cross-browser testing via Ultrafast Grid, and retrieving structured test results.

Remote MCP Server Authless

A Cloudflare Workers-based Model Context Protocol server without authentication requirements, allowing users to deploy and customize AI tools that can be accessed from Claude Desktop or Cloudflare AI Playground.

Respira for WordPress

MCP server for AI-assisted WordPress editing across 12 page builders. 172 tools for content management, page builder editing, WooCommerce, SEO analysis, accessibility scanning, and site intelligence. Edits native builder formats (Elementor, Bricks, Divi, Gutenberg, Beaver Builder, and 7 more) with duplicate-before-edit safety, optimistic locking, and surgical element-level operations

Configurable Puppeteer MCP Server

一个模型上下文协议（Model Context Protocol）服务器，它使用 Puppeteer 提供浏览器自动化功能，并通过环境变量配置选项，从而使大型语言模型（LLM）能够与网页交互、截取屏幕截图，并在浏览器环境中执行 JavaScript。

ETH Price Current Server

A minimal Model Context Protocol (MCP) server that fetches the current Ethereum (ETH) price in USD. Data source: the public CoinGecko API (no API key required). This MCP is designed to simulate malicious behavior, specifically an attempt to mislead LLM to return incorrect results.

XERT Cycling Training

Connect Claude to XERT cycling analytics - access fitness signature (FTP, LTP, HIE), training load, workouts, and activities.

Bureau of Economic Analysis (BEA) MCP Server

Provides access to comprehensive U.S. economic data including GDP, personal income, and regional statistics via the Bureau of Economic Analysis API. It enables users to query datasets and retrieve specific economic indicators for states, counties, and industries through natural language.

Structured Thinking

一个统一的 MCP 服务器，用于结构化思维工具，包括模板思维和验证思维。 (Alternatively, depending on the specific context and target audience, you could also say:) 一个整合的 MCP 服务器，提供结构化思维工具，例如模板思维和验证思维。

mcp_server

Okay, I understand. You want me to describe how to implement a "weather MCP server" that can be called by a client IDE like Cursor. Here's a breakdown of the concept, implementation considerations, and a simplified example (using Python and a basic HTTP API) to illustrate the core ideas. **What is an MCP Server (in this context)?** In this scenario, "MCP" likely refers to a *Microservice Communication Protocol* or a similar concept. It means you're building a small, independent service (the weather server) that provides weather information and communicates with other applications (like the Cursor IDE) using a defined protocol. In practice, this often translates to a RESTful API over HTTP. **Key Components** 1. **Weather Data Source:** * This is where your server gets the actual weather information. You'll likely use a third-party weather API (e.g., OpenWeatherMap, AccuWeather, WeatherAPI.com). These APIs typically require you to sign up for an account and obtain an API key. * Consider caching the weather data to reduce the number of API calls and improve response times. 2. **Server-Side Implementation (e.g., Python with Flask/FastAPI):** * This is the core of your weather server. It handles incoming requests, fetches weather data from the data source, and formats the response. * **Framework Choice:** * **Flask:** A lightweight and flexible framework, good for simple APIs. * **FastAPI:** A modern, high-performance framework with automatic data validation and API documentation (using OpenAPI/Swagger). Generally preferred for new projects. * **API Endpoints:** You'll define endpoints like: * `/weather?city={city_name}`: Returns weather information for a specific city. * `/weather?zip={zip_code}`: Returns weather information for a specific zip code. * `/forecast?city={city_name}`: Returns a weather forecast for a specific city. 3. **Client-Side Integration (in Cursor IDE):** * The Cursor IDE (or any other client) will need to make HTTP requests to your weather server's API endpoints. * This might involve writing code within Cursor (e.g., using JavaScript or Python within a Cursor extension) to: * Get user input (e.g., the city name). * Construct the API request URL. * Send the request to the weather server. * Parse the JSON response from the server. * Display the weather information in the Cursor IDE. **Implementation Steps (Simplified Example with Python and Flask)** **1. Set up your environment:** ```bash # Create a project directory mkdir weather_server cd weather_server # Create a virtual environment (recommended) python3 -m venv venv source venv/bin/activate # On Linux/macOS # venv\Scripts\activate # On Windows # Install Flask and requests (for making HTTP requests to the weather API) pip install Flask requests ``` **2. `weather_server.py` (Flask Server):** ```python from flask import Flask, request, jsonify import requests import os app = Flask(__name__) # Replace with your actual OpenWeatherMap API key API_KEY = os.environ.get("OPENWEATHERMAP_API_KEY") or "YOUR_OPENWEATHERMAP_API_KEY" # Get from environment variable or hardcode (not recommended for production) BASE_URL = "https://api.openweathermap.org/data/2.5/weather" def get_weather_data(city): """Fetches weather data from OpenWeatherMap.""" params = { "q": city, "appid": API_KEY, "units": "metric", # Use Celsius } try: response = requests.get(BASE_URL, params=params) response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx) data = response.json() return data except requests.exceptions.RequestException as e: print(f"Error fetching weather data: {e}") return None @app.route("/weather") def weather(): """API endpoint to get weather by city.""" city = request.args.get("city") if not city: return jsonify({"error": "City parameter is required"}), 400 weather_data = get_weather_data(city) if weather_data: # Extract relevant information temperature = weather_data["main"]["temp"] description = weather_data["weather"][0]["description"] humidity = weather_data["main"]["humidity"] wind_speed = weather_data["wind"]["speed"] return jsonify({ "city": city, "temperature": temperature, "description": description, "humidity": humidity, "wind_speed": wind_speed }) else: return jsonify({"error": "Could not retrieve weather data for that city"}), 500 if __name__ == "__main__": app.run(debug=True) # Don't use debug=True in production! ``` **3. Running the Server:** ```bash # Set your OpenWeatherMap API key (replace with your actual key) export OPENWEATHERMAP_API_KEY="YOUR_OPENWEATHERMAP_API_KEY" # Linux/macOS # set OPENWEATHERMAP_API_KEY="YOUR_OPENWEATHERMAP_API_KEY" # Windows # Run the Flask server python weather_server.py ``` **4. Example Client-Side Code (Conceptual - in Cursor IDE):** This is a *very* simplified example of how you *might* integrate this into Cursor. The exact implementation will depend on Cursor's extension API and how you want to display the information. This assumes you can execute JavaScript or Python code within Cursor. ```javascript // Example JavaScript code (Conceptual - adapt to Cursor's API) async function getWeather(city) { const apiUrl = `http://127.0.0.1:5000/weather?city=${city}`; // Replace with your server's address try { const response = await fetch(apiUrl); const data = await response.json(); if (response.ok) { // Display the weather information in the Cursor IDE console.log(`Weather in ${data.city}:`); console.log(`Temperature: ${data.temperature}°C`); console.log(`Description: ${data.description}`); console.log(`Humidity: ${data.humidity}%`); console.log(`Wind Speed: ${data.wind_speed} m/s`); // You'd need to use Cursor's API to actually display this in the editor or a panel. // For example, Cursor might have a function like: // cursor.showInformationMessage(`Weather in ${data.city}: ...`); } else { console.error(`Error: ${data.error}`); // Display an error message in Cursor } } catch (error) { console.error("Error fetching weather:", error); // Display a network error in Cursor } } // Example usage: const cityName = "London"; // Or get the city from user input in Cursor getWeather(cityName); ``` **Explanation and Improvements** * **Error Handling:** The code includes basic error handling (checking for API errors, missing city parameter). Robust error handling is crucial for production. * **API Key Security:** *Never* hardcode your API key directly in the code, especially if you're sharing it. Use environment variables (as shown) or a configuration file. * **Asynchronous Operations:** Use `async/await` (as in the JavaScript example) to avoid blocking the UI thread while waiting for the API response. * **Data Validation:** Use a library like `marshmallow` (in Python) or a similar validation library in your chosen language to validate the data received from the weather API. This helps prevent unexpected errors. * **Caching:** Implement caching to store frequently accessed weather data. This reduces the load on the weather API and improves response times. You could use a simple in-memory cache (for small-scale deployments) or a more robust caching solution like Redis or Memcached. * **Rate Limiting:** Be aware of the rate limits imposed by the weather API you're using. Implement rate limiting in your server to avoid exceeding the limits and getting your API key blocked. * **Logging:** Use a logging library (e.g., `logging` in Python) to log important events, errors, and debugging information. * **API Documentation:** Use a tool like Swagger (with FastAPI) to automatically generate API documentation. This makes it easier for other developers to use your weather server. * **Deployment:** Consider deploying your weather server to a cloud platform like AWS, Google Cloud, or Azure. **Chinese Translation of Key Concepts** * **Weather MCP Server:** 天气 MCP 服务器 (Tiānqì MCP fúwùqì) * **Microservice Communication Protocol:** 微服务通信协议 (Wēi fúwù tōngxìn xiéyì) * **API Endpoint:** API 端点 (API duāndiǎn) * **RESTful API:** RESTful API (RESTful API) (The term is often used directly in Chinese as well) * **API Key:** API 密钥 (API mìyào) * **Data Source:** 数据源 (shùjù yuán) * **Caching:** 缓存 (huǎncún) * **Rate Limiting:** 速率限制 (sùlǜ xiànzhì) * **Error Handling:** 错误处理 (cuòwù chǔlǐ) * **Environment Variable:** 环境变量 (huánjìng biànliàng) **Important Considerations for Cursor Integration** * **Cursor's Extension API:** The most important thing is to understand Cursor's extension API. How can you create extensions, access the editor, display information, and get user input? Refer to Cursor's official documentation for this. * **Security:** Be very careful about security when integrating with an IDE. Avoid storing sensitive information (like API keys) directly in the extension code. Use secure storage mechanisms provided by the IDE or the operating system. * **User Experience:** Design the integration to be as seamless and intuitive as possible for the user. Consider how the weather information will be displayed (e.g., in a tooltip, a panel, or directly in the editor). This detailed explanation and example should give you a solid foundation for building your weather MCP server and integrating it with Cursor. Remember to adapt the code and concepts to your specific needs and the capabilities of the Cursor IDE. Good luck!

Kibana MCP Server

Enables AI assistants to interact with Kibana dashboards, visualizations, and Elasticsearch data through read-only resources and executable tools for searching logs, exporting dashboards, and querying data.

Salesforce MCP Server

A comprehensive server that transforms Claude Desktop into a Salesforce IDE for managing metadata, executing SOQL queries, and automating multi-org operations. It provides 60 optimized tools for intelligent debugging, bulk data management, and Apex testing through natural language commands.

Skyvern MCP

Skyvern MCP server lets AI agents control a real browser to navigate websites, fill forms, authenticate, and extract structured data. Supports multi-step automation workflows via natural language.

Xero MCP Server

一个允许客户端与 Xero 会计软件交互的 MCP 服务器。

Spiral MCP Server

一个模型上下文协议（Model Context Protocol）服务器实现，它为与 Spiral 的语言模型交互提供了一个标准化的接口，并提供从提示词、文件或 Web URL 生成文本的工具。

Medikode Medical Coding MCP Server

Enables AI assistants to access Medikode's medical coding platform for validating CPT/ICD-10 codes, performing chart quality assurance, parsing EOBs, calculating RAF scores, and extracting HCC codes from clinical documentation.

MCP Server Boilerplate

A starter template for building custom MCP servers that can integrate with Claude Desktop, Cursor, and other AI assistants. Provides example tools, TypeScript support, and automated publishing workflow to help developers create their own tools and resource providers.

MCP Agent & Server Ecosystem

An autonomous agent ecosystem that leverages Groq for high-performance reasoning and Playwright for web automation via the Model Context Protocol. It features an interactive CLI and a custom server providing a run_task tool for orchestrating multi-server workflows.

Image Process MCP Server

That's a good translation! It's accurate and concise. Here are a couple of minor variations, depending on the nuance you want to convey: * **More literal:** 一个使用 Sharp 库提供图像处理功能的图像处理 MCP 服务器。 (This is closer to a word-for-word translation.) * **Slightly more natural flow:** 这是一个基于 Sharp 库的图像处理 MCP 服务器，用于提供图像处理功能。 (This emphasizes that the server is *based on* the Sharp library.) All three are perfectly understandable. Your original translation is excellent.

Effect CLI - Model Context Protocol

MCP 服务器，以命令行工具的形式公开

MCP DateTime Server

Provides current local datetime information with timezone support. Serves as a minimal blueprint for building simple, single-purpose MCP servers.

minesweeper-mcp

A stdio-based MCP server that enables users to play and manage Minesweeper games through a Rails 8 REST API. It provides tools to start games, track states, and perform actions like opening cells and flagging mines.

React USWDS MCP Server

Indexes the locally-installed @trussworks/react-uswds package and helps code assistants discover components, inspect props, generate correct imports and usage snippets, and suggest appropriate components for UI use cases.

Tarot MCP Server

Provides tarot card reading capabilities with a complete 78-card deck, multiple spread layouts (Celtic Cross, Past-Present-Future, etc.), and detailed card interpretations for divination and daily guidance.

mcp-cli-catalog

An MCP server that publishes CLI tools on your machine for discoverability by LLMs