Discover Awesome MCP Servers

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.

Teable MCP Server

Connects Teable no-code databases to LLMs, enabling AI agents to query records, explore schema structures, retrieve data history, and interact with spaces, bases, tables, and views using natural language.

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_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!

applemail-mcp-server

A local MCP server that lets Claude read macOS Mail.app via JXA, with tools for listing accounts/mailboxes, searching and reading messages. Read-only by default, with optional opt-in write tools for compose drafts and Apple Calendar events.

jira-minimal-mcp

Minimal MCP server for Jira with configurable tools to reduce token usage, starting from ~150 tokens for basic issue retrieval.

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.

Google Workspace MCP Server

Enables management of Google Workspace apps (Docs, Sheets, Gmail, Calendar, Drive) from the command line via Gemini CLI.

Subwatch MCP

Enables reading public Reddit posts and comments on demand, with tools to search, get recent posts, post details, top comments, and server status. Runs on Cloudflare Workers for use with Claude and Open WebUI.

demandscope

MCP server that provides demand-signal scanning tools from GitHub, Hacker News, npm, and PyPI to validate digital-product and dev-tool ideas. Enables agents to query repo signals, trends, and download counts via stdio JSON-RPC.

flstudio-mcp-mac

Enables controlling FL Studio on macOS via MCP, including transport, mixer, channel, MIDI export, and Piano Roll note insertion.

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.

musfiraai-mcp-server

Exposes Musfiraai's company info, services, AI stack, FAQ, reviews, and contact details as MCP tools/resources/prompts, enabling any MCP-compatible AI client to query this data via natural language.

Score de Crédito

Enables credit score and risk analysis queries for Brazilian individuals (CPF) and companies (CNPJ) via MCP over HTTP, with a single read-only tool and prepaid usage.

VA-MCP

An MCP server for checking OWASP Top 10 vulnerabilities during API development testing. It analyzes API information and returns security assessment results to help developers identify potential security issues.

mcp-cli-catalog

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

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.

Dynatrace MCP Server

Enables interaction with Dynatrace Grail and API endpoints by executing DQL queries, retrieving logs, reporting active problems, and querying metrics.

context-ledger

Provides local, explicitly scoped memory for coding agents via MCP, storing durable project knowledge in a per-repository SQLite database with tools to record, search, and retrieve context.

samskriti-project

A local MCP server that enables multiple AI coding tools to share structured project state (decisions, tasks, bugs) so they coordinate without re-explaining.

CRM Agent Tools

Provides Claude agents with CRM contact lookup, action logging, and prompt cost auditing tools. Includes a Streamlit UI to demo the same tools without an MCP client.

Hyperion V2

Universal MCP server that enables any LLM agent (Claude, Cursor, Cline) to control a real Chrome browser with 5 perception engines, resilient heartbeat, and real-time vision streaming.

Gopher & Gemini MCP Server

Enables AI assistants to browse and interact with both Gopher and Gemini protocol resources safely and efficiently.

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.

pixso-mcp-server

Brings Pixso design context into AI coding workflows, enabling AI assistants to understand design layers, component variants, variables, and local styles for design-to-code tasks.

TickDB MCP

Unified real-time & historical market data API for Forex, stocks (US/HK/A-share), crypto, indices & precious metals. Tick, order book depth & K-line via REST + WebSocket. AI-native: MCP server, Skill & CLI.

herald

An MCP server that allows AI assistants to send formatted text, files, and updates to Telegram topics, and to capture incoming messages from work chats into a local inbox for retrieval.

withings-mcp

Read-only Model Context Protocol server for Withings weight and body-composition history, providing normalized measurements, weight history, and latest measurements via OAuth 2.0.

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.

temporal-mcp-server

Enables AI assistants to observe and manage Temporal workflows, including listing, starting, signaling, resetting, terminating, querying, and retrieving history, with support for wait_for_activity and webhook utilities.