Discover Awesome MCP Servers

email-agent-mcp-server

Enables AI-powered Gmail triage: fetches unread emails, classifies them, drafts replies for important ones, and sends only after user approval via conversational review. Exposes tools for checking unread emails and submitting review feedback from Claude Desktop or any MCP client.

mcp-gtags-server

Provides fast, indexed code navigation (definitions, references, callers, etc.) for AI coding agents by leveraging GNU Global (gtags), dramatically reducing context noise compared to grep.

@deepidv/mcp-server

Enables AI agents to perform identity verification, KYC/KYB, PEP & sanctions screening, bank statement analysis, and workflow automation via the Model Context Protocol.

Hacker News MCP Server

Enables LLMs to browse Hacker News stories, inspect items, and look up user profiles via the official Firebase API.

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.

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.

Reddit MCP

Enables browsing, searching, and reading Reddit posts, comments, and subreddits through Reddit's API using PRAW.

XERT Cycling Training

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

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.

fallmind-v2-mcp

MCP server for foldkit that exposes the 7-prime spine, 7 κ-bands, and 6 fold operations as tools and resources, enabling interaction with fold state analysis and manipulation via natural language in any MCP client.

Slack Universal MCP Server

Provides a standardized interface for interacting with Slack's tools and services through a unified API, enabling integration with MCP-compliant applications.

Bitbucket Cloud MCP Server

Enables AI assistants to read Bitbucket Cloud pull requests and diffs through natural conversation.

Mcp Akshare

AKShare adalah pustaka antarmuka data keuangan berbasis Python yang bertujuan untuk mengimplementasikan serangkaian alat untuk data fundamental, data pasar real-time dan historis, dan data turunan dari produk keuangan seperti saham, futures, opsi, dana, valuta asing, obligasi, indeks, dan mata uang kripto, mulai dari pengumpulan data, pembersihan data, hingga penyimpanan data, terutama untuk tujuan penelitian akademis.

mcp_server

Okay, I understand. You're looking for a weather microservice (MCP likely refers to Microservice Communication Protocol) that can be accessed by a client IDE like Cursor. Here's a breakdown of how you could approach building such a system, along with considerations for Indonesian users: **1. Core Functionality: The Weather Microservice** * **Technology Stack:** * **Language:** Python (with Flask or FastAPI), Node.js (with Express), Go, or Java (with Spring Boot) are all good choices. Python is often favored for its ease of use and rich ecosystem of libraries for data handling. * **Framework:** Flask or FastAPI (Python), Express (Node.js), Spring Boot (Java) - These frameworks simplify building web APIs. * **Weather API:** You'll need to integrate with a third-party weather API. Popular options include: * **OpenWeatherMap:** Free and paid tiers. Good coverage, including Indonesia. * **AccuWeather:** Commercial API, generally reliable. * **WeatherAPI.com:** Another commercial option. * **Visual Crossing Weather:** Offers historical and forecast data. * **Data Storage (Optional):** If you want to cache weather data or store historical information, consider a database like PostgreSQL, MySQL, or MongoDB. Caching can significantly improve response times and reduce API usage costs. * **API Endpoints:** * `/weather/city/{city_name}`: Returns the current weather for a given city. Example: `/weather/city/Jakarta` * `/weather/coordinates/{latitude}/{longitude}`: Returns the current weather for a given latitude and longitude. Example: `/weather/coordinates/-6.2088/106.8456` (Jakarta coordinates) * `/forecast/city/{city_name}`: Returns a weather forecast for a given city. * `/forecast/coordinates/{latitude}/{longitude}`: Returns a weather forecast for given coordinates. * **Data Format:** JSON (JavaScript Object Notation) is the standard for API responses. * **Example (Python with Flask):** ```python from flask import Flask, jsonify import requests import os app = Flask(__name__) # Replace with your actual API key from OpenWeatherMap or another provider API_KEY = os.environ.get("WEATHER_API_KEY") or "YOUR_API_KEY" BASE_URL = "https://api.openweathermap.org/data/2.5/weather" # Example: OpenWeatherMap @app.route('/weather/city/<city_name>') def get_weather_by_city(city_name): try: url = f"{BASE_URL}?q={city_name}&appid={API_KEY}&units=metric" # Use metric for Celsius response = requests.get(url) response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx) data = response.json() return jsonify(data) except requests.exceptions.RequestException as e: return jsonify({'error': str(e)}), 500 except Exception as e: return jsonify({'error': str(e)}), 500 @app.route('/weather/coordinates/<latitude>/<longitude>') def get_weather_by_coordinates(latitude, longitude): try: url = f"{BASE_URL}?lat={latitude}&lon={longitude}&appid={API_KEY}&units=metric" response = requests.get(url) response.raise_for_status() data = response.json() return jsonify(data) except requests.exceptions.RequestException as e: return jsonify({'error': str(e)}), 500 except Exception as e: return jsonify({'error': str(e)}), 500 if __name__ == '__main__': app.run(debug=True) ``` **Explanation:** * **Flask:** Sets up a simple web server. * **`API_KEY`:** Crucially, store your API key securely (environment variable is best). *Never* hardcode it directly into your code that you commit to a repository. * **`BASE_URL`:** The base URL for the weather API you're using. * **`get_weather_by_city` and `get_weather_by_coordinates`:** These are the API endpoints. They take the city name or coordinates as input, make a request to the weather API, and return the JSON response. * **Error Handling:** Includes basic error handling to catch network issues and API errors. * **`units=metric`:** Important for Indonesian users (and most of the world) to get temperatures in Celsius. **2. Client IDE Integration (Cursor Example)** * **Cursor Custom Command/Extension:** Cursor allows you to create custom commands or extensions. You'll need to write code (likely in JavaScript/TypeScript) that: 1. Takes user input (e.g., city name, coordinates). 2. Makes an HTTP request to your weather microservice's API endpoint. 3. Parses the JSON response from the microservice. 4. Displays the weather information in a user-friendly way within the Cursor IDE. * **Example (Conceptual Cursor Command):** ```javascript // (Conceptual - this is not complete, runnable Cursor code) async function getWeather(city) { const apiUrl = `http://localhost:5000/weather/city/${city}`; // Replace with your microservice URL try { const response = await fetch(apiUrl); const data = await response.json(); if (response.ok) { // Format and display the weather data in Cursor const temperature = data.main.temp; const description = data.weather[0].description; console.log(`Weather in ${city}: ${temperature}°C, ${description}`); // Or use Cursor's UI API to display it better } else { console.error(`Error: ${data.error}`); } } catch (error) { console.error(`Network error: ${error}`); } } // Example usage (triggered by a Cursor command) getWeather("Jakarta"); ``` **Key Considerations for Cursor Integration:** * **Cursor API:** You'll need to consult the Cursor documentation to understand how to create custom commands, access the editor, and display information. * **Asynchronous Operations:** Fetching data from the API is an asynchronous operation. Use `async/await` to handle it properly. * **Error Handling:** Robust error handling is essential. * **User Interface:** Think about how to present the weather information clearly and concisely within the IDE. **3. Indonesian Considerations (Localization)** * **City Names:** Handle Indonesian city names correctly (e.g., "Jakarta" vs. "DKI Jakarta"). Consider using a city name database or a geocoding API to map variations to the correct location. * **Language:** If you want to provide weather information in Indonesian, you'll need to: * Use a weather API that supports Indonesian language output (some do). * Translate the weather descriptions yourself (e.g., "clear sky" to "langit cerah"). This is more complex but gives you full control. * **Units:** Celsius is the standard in Indonesia, so ensure your API requests use `units=metric`. * **Time Zones:** Be mindful of time zones. Jakarta is in GMT+7. Display times in the user's local time zone. **4. Deployment** * **Microservice:** Deploy your weather microservice to a cloud platform like: * **Heroku:** Easy to deploy Python, Node.js, and other applications. * **AWS (Amazon Web Services):** More complex but very powerful. Use services like EC2, Lambda, and API Gateway. * **Google Cloud Platform (GCP):** Similar to AWS. Use services like Cloud Run, Cloud Functions, and API Gateway. * **Azure:** Microsoft's cloud platform. * **Cursor Extension:** The deployment of the Cursor extension will depend on how Cursor allows extensions to be distributed (e.g., a marketplace, manual installation). **5. Scalability and Reliability** * **Caching:** Implement caching to reduce API calls and improve response times. * **Monitoring:** Monitor your microservice's performance and error rates. * **Load Balancing:** If you expect a lot of traffic, use a load balancer to distribute requests across multiple instances of your microservice. * **Rate Limiting:** Implement rate limiting to prevent abuse of your API. **Example Indonesian Weather Response (Hypothetical):** ```json { "city": "Jakarta", "temperature": 30, "description": "Cerah berawan", // Partly cloudy "humidity": 70, "wind_speed": 5, "time": "2023-10-27 14:30 WIB" } ``` **In Indonesian:** Implementasi server MCP cuaca yang dapat dipanggil oleh IDE klien seperti Cursor. **Explanation of the Indonesian Translation:** * **Implementasi:** Implementation * **server MCP cuaca:** weather MCP server * **yang dapat dipanggil:** that can be called * **oleh IDE klien:** by a client IDE * **seperti Cursor:** like Cursor **Key Takeaways:** * Start with a simple weather microservice using a framework like Flask or FastAPI. * Integrate with a reliable weather API. * Focus on getting the core functionality working first. * Then, create a Cursor command/extension to access the microservice. * Consider Indonesian localization (city names, language, units). * Deploy your microservice to a cloud platform. * Think about scalability and reliability as your application grows. This is a comprehensive outline. You'll need to break it down into smaller tasks and implement each part step by step. Good luck!

claude-session-bus

Enables coordination and communication between multiple Claude Code sessions across machines via a chat server, providing tools for sending messages, waiting for responses, and managing session status.

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.

AgentGuard MCP

A protected Model Context Protocol server that gives AI agents distinct machine identities, enforces least-privilege OAuth permissions, applies contextual authorization policies, and pauses sensitive actions for human approval.

Image Process MCP Server

Sebuah Server MCP untuk pemrosesan gambar yang menggunakan pustaka Sharp untuk menyediakan fungsionalitas manipulasi gambar. (Atau, bisa juga: Server MCP untuk pemrosesan gambar yang memanfaatkan *library* Sharp untuk menyediakan fungsi manipulasi gambar.)

Nano Banana Pro AI MCP Server

Exposes the Nano Banana Pro AI knowledge surface (image generation workflows, styles, pricing, FAQ, official links) to MCP-compatible AI clients such as Claude Desktop, Cursor, and Windsurf, enabling querying of image editing capabilities and pricing information without API keys.

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.

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.

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.

kd-mcp

Controls kd.exe for KDNET kernel debugging on Windows, often paired with winrm-mcp for guest VM setup over WinRM.

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.

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.

mcp-cli-catalog

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