Discover Awesome MCP Servers

EVM MCP Server

Sebuah server komprehensif yang memungkinkan agen AI untuk berinteraksi dengan berbagai jaringan blockchain yang kompatibel dengan EVM melalui antarmuka terpadu, mendukung resolusi ENS, operasi token, dan interaksi kontrak pintar.

PHP MCP Protocol Server

Servidor MCP para PHP Universal - integra PHP com o protocolo Model Context Protocol

perplexity-server MCP Server

Perplexity MCP Server for Cline

Fused MCP Agents: Setting up MCP Servers for Data

Sebuah server MCP berbasis Python yang memungkinkan Claude dan LLM lainnya untuk menjalankan kode Python arbitrer secara langsung melalui aplikasi Claude desktop Anda, memungkinkan ilmuwan data untuk menghubungkan LLM ke API dan kode yang dapat dieksekusi.

MCP Image Generation Server

Implementasi Go dari peralatan server MCP (Model Context Protocol)

Strava MCP Server

Server Protokol Konteks Model yang memungkinkan pengguna mengakses data kebugaran Strava, termasuk aktivitas pengguna, detail aktivitas, segmen, dan papan peringkat melalui antarmuka API terstruktur.

Selector Mcp Server

Sebuah server Model Context Protocol (MCP) yang memungkinkan obrolan AI interaktif secara real-time dengan Selector AI melalui server yang mendukung streaming dan klien berbasis Docker yang berkomunikasi melalui stdin/stdout.

Data.gov MCP Server

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Github Action Trigger Mcp

Server Protokol Konteks Model yang memungkinkan integrasi dengan GitHub Actions, memungkinkan pengguna untuk mengambil tindakan (actions) yang tersedia, mendapatkan informasi detail tentang tindakan tertentu, memicu peristiwa pengiriman alur kerja (workflow dispatch events), dan mengambil rilis repositori.

MCP Demo

Okay, I can help you outline the concept of an MCP (Microservice Communication Protocol) server for fetching aviation weather data and provide a basic example using Python and Flask. Keep in mind that this is a simplified illustration. A real-world implementation would involve more robust error handling, security, and potentially more sophisticated data processing. **Conceptual Overview** 1. **Data Source:** The server will need to access a reliable source of aviation weather data. Common sources include: * **NOAA Aviation Weather Center (AWC):** Provides METARs, TAFs, and other aviation weather products. Often accessed via their XML/text feeds. * **AviationWeather.gov:** Another NOAA resource. * **Commercial Aviation Weather APIs:** Many companies offer paid APIs with more features and potentially better reliability. 2. **MCP Server (Flask Example):** The server will expose an endpoint (e.g., `/weather`) that accepts requests for weather data. It will: * Receive a request (e.g., with an airport ICAO code like "KJFK"). * Fetch the weather data from the chosen source. * Parse the data (e.g., from XML or text). * Return the data in a structured format (e.g., JSON). 3. **Client:** A client application (e.g., a web app, a mobile app, or another microservice) will send requests to the MCP server and display the weather information to the user. **Simplified Python/Flask Example** ```python from flask import Flask, request, jsonify import requests import xml.etree.ElementTree as ET # For parsing XML (if using NOAA) app = Flask(__name__) # Replace with your actual data source URL (NOAA AWC example) NOAA_URL = "https://www.aviationweather.gov/adds/dataserver_current/httpparam?dataSource=metars&requestType=retrieve&format=xml&hoursBeforeNow=1&mostRecentForEachStation=true&stationString=" def fetch_metar_from_noaa(icao): """Fetches METAR data from NOAA AWC for a given ICAO code.""" try: url = NOAA_URL + icao response = requests.get(url) response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx) xml_content = response.content root = ET.fromstring(xml_content) metar_elements = root.findall(".//METAR") # Find all METAR elements if not metar_elements: return None # No METAR found for this ICAO metar_data = [] for metar_element in metar_elements: metar_text = metar_element.find("raw_text").text station_id = metar_element.find("station_id").text observation_time = metar_element.find("observation_time").text metar_data.append({ "station_id": station_id, "observation_time": observation_time, "raw_text": metar_text }) return metar_data except requests.exceptions.RequestException as e: print(f"Error fetching data: {e}") return None except ET.ParseError as e: print(f"Error parsing XML: {e}") return None except Exception as e: print(f"An unexpected error occurred: {e}") return None @app.route('/weather', methods=['GET']) def get_weather(): """ Endpoint to fetch weather data for a given ICAO airport code. Example: /weather?icao=KJFK """ icao = request.args.get('icao') if not icao: return jsonify({"error": "ICAO code is required"}), 400 weather_data = fetch_metar_from_noaa(icao) if weather_data: return jsonify(weather_data) else: return jsonify({"error": f"Could not retrieve weather data for {icao}"}), 404 if __name__ == '__main__': app.run(debug=True) # Use debug=False in production ``` **Explanation:** 1. **Imports:** Imports necessary libraries: `Flask` for the web server, `requests` for making HTTP requests, `xml.etree.ElementTree` for parsing XML (if using NOAA). 2. **`NOAA_URL`:** This is the URL for the NOAA AWC data server. You'll need to adjust this if you use a different data source. The `stationString=` parameter is where you'll append the ICAO code. 3. **`fetch_metar_from_noaa(icao)`:** * Constructs the URL with the ICAO code. * Uses `requests.get()` to fetch the data. * Uses `response.raise_for_status()` to check for HTTP errors (4xx, 5xx). * Parses the XML response using `xml.etree.ElementTree`. * Extracts the METAR data (raw text, station ID, observation time). * Returns the data as a list of dictionaries. * Includes error handling for network issues, XML parsing errors, and other exceptions. 4. **`@app.route('/weather', methods=['GET'])`:** This defines the `/weather` endpoint, which accepts GET requests. 5. **`get_weather()`:** * Retrieves the `icao` parameter from the request's query string (e.g., `/weather?icao=KJFK`). * Calls `fetch_metar_from_noaa()` to get the weather data. * Returns the data as a JSON response using `jsonify()`. * Includes error handling: * If the ICAO code is missing, it returns a 400 error. * If the weather data cannot be retrieved, it returns a 404 error. 6. **`if __name__ == '__main__':`:** Starts the Flask development server. **Important:** Use `debug=False` in a production environment. **How to Run:** 1. **Install Flask and requests:** ```bash pip install Flask requests ``` 2. **Save the code:** Save the code as a Python file (e.g., `weather_server.py`). 3. **Run the server:** ```bash python weather_server.py ``` 4. **Test the endpoint:** Open a web browser or use `curl` to access the endpoint: ``` http://127.0.0.1:5000/weather?icao=KJFK ``` (Replace `KJFK` with the ICAO code you want to query.) **Important Considerations and Improvements:** * **Error Handling:** The example includes basic error handling, but you should add more robust error logging and reporting. * **Data Source:** Choose a reliable data source that meets your needs. Consider commercial APIs for better performance and features. * **Data Parsing:** The XML parsing in the example is basic. You might need to handle different XML structures or use a more sophisticated XML parsing library. If using a JSON API, the parsing will be simpler. * **Caching:** Implement caching to reduce the load on the data source and improve response times. You can use Flask's built-in caching or a dedicated caching library like Redis or Memcached. * **Rate Limiting:** Implement rate limiting to prevent abuse of your API. * **Authentication/Authorization:** If you need to restrict access to your API, implement authentication and authorization. * **Configuration:** Use environment variables or a configuration file to store sensitive information like API keys and database credentials. * **Deployment:** Deploy the server to a production environment (e.g., AWS, Google Cloud, Azure). Use a WSGI server like Gunicorn or uWSGI. * **Monitoring:** Monitor the server's performance and health. * **Asynchronous Operations:** For high-volume requests, consider using asynchronous operations (e.g., with `asyncio` or Celery) to improve performance. * **Data Validation:** Validate the ICAO code and other input parameters to prevent errors. * **Data Transformation:** You might need to transform the data from the data source into a format that is more suitable for your client applications. **MCP (Microservice Communication Protocol) Considerations:** * **Protocol:** While this example uses HTTP/JSON, you could use other protocols like gRPC or Thrift for more efficient communication between microservices. * **Service Discovery:** In a microservices architecture, you'll need a mechanism for service discovery (e.g., Consul, etcd, or Kubernetes DNS). This allows your client applications to find the MCP server dynamically. * **API Gateway:** An API gateway can provide a single entry point for all your microservices and handle tasks like authentication, rate limiting, and request routing. This example provides a starting point for building an MCP server for fetching aviation weather data. Remember to adapt it to your specific requirements and consider the important considerations mentioned above.

NN-GitHubTestRepo

dibuat dari demo server MCP

HANA Cloud MCP Server

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better-auth-mcp-server MCP Server

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repo-to-txt-mcp

Here are a few ways to translate "MCP server for analyzing and converting Git repositories to text files for LLM context" into Indonesian, with slightly different nuances: **Option 1 (Most straightforward):** * **Server MCP untuk menganalisis dan mengonversi repositori Git menjadi berkas teks untuk konteks LLM.** * This is a direct translation, using common Indonesian words. "Berkas" is a good general word for "file." **Option 2 (More descriptive, emphasizing the purpose):** * **Server MCP untuk menganalisis dan mengubah repositori Git menjadi format berkas teks, yang digunakan sebagai konteks untuk LLM.** * This version adds "yang digunakan sebagai konteks untuk LLM" (which is used as context for LLM) to make the purpose clearer. **Option 3 (Using "repository" instead of "repositori"):** * **Server MCP untuk menganalisis dan mengonversi repository Git menjadi berkas teks untuk konteks LLM.** * "Repository" is often used directly in Indonesian, especially in technical contexts. This might sound more natural to some Indonesian speakers familiar with Git. **Option 4 (More formal):** * **Server MCP untuk analisis dan konversi repositori Git menjadi berkas teks guna keperluan konteks LLM.** * This uses the more formal "analisis" and "konversi" instead of "menganalisis" and "mengonversi," and "guna keperluan" (for the purpose of) instead of "untuk." **Which one is best depends on the audience:** * For a general audience, **Option 1** is probably the best. * For a more technical audience familiar with Git, **Option 3** might be preferable. * If you want to be very clear about the purpose, **Option 2** is a good choice. * For a formal document, **Option 4** is suitable. Therefore, I recommend **Option 1: Server MCP untuk menganalisis dan mengonversi repositori Git menjadi berkas teks untuk konteks LLM.** as a good starting point.

Google Home MCP Server

Mirror of

mcp-server-restart

Mirror of

Semantic Scholar MCP Server

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Model Context Protocol (MCP)

The Model Context Protocol is an open standard that enables developers to build secure, two-way connections between their data sources and AI-powered tools. The architecture is straightforward: developers can either expose their data through MCP servers or build AI applications (MCP clients) that connect to these servers.

MCP GO Tools

A Go-focused Model Context Protocol (MCP) server that provides idiomatic Go code generation, style guidelines, and best practices. This tool helps Language Models understand and generate high-quality Go code following established patterns and conventions.

Math-MCP

Server Protokol Konteks Model yang menyediakan fungsi matematika dan statistik dasar untuk LLM, memungkinkan mereka melakukan perhitungan numerik yang akurat melalui API sederhana.

DVMCP: Data Vending Machine Context Protocol

DVMCP is a bridge implementation that connects Model Context Protocol (MCP) servers to Nostr's Data Vending Machine (DVM) ecosystem

GitHub MCP Server

Mirror of

MCP Server Playground

Server MCP berbasis TypeScript yang dirancang untuk eksperimen dan integrasi dengan Calude Desktop dan Cursor IDE, menawarkan *playground* modular untuk memperluas kemampuan server.

MCP Server Reddit

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mcp-google-sheets: A Google Sheets MCP server

Server Protokol Konteks Model yang terintegrasi dengan Google Drive dan Google Sheets, memungkinkan pengguna untuk membuat, membaca, memperbarui, dan mengelola spreadsheet melalui perintah bahasa alami.

MailchimpMCP

Some utilities for developing an MCP server for the Mailchimp API

GraphQL MCP Server

Sebuah server TypeScript yang menyediakan Claude AI dengan akses tanpa hambatan ke API GraphQL mana pun melalui Model Context Protocol.