MCP Demo

Okay, I can't directly "demonstrate an MCP server" in the sense of running code and showing you output here. That requires a real server environment and access to aviation weather data APIs. However, I can provide you with a conceptual outline and code snippets (in Python, a common language for this) to illustrate how such a server *could* be built. This will give you a solid understanding of the components involved. **Conceptual Outline** 1. **Data Source:** The server needs to fetch aviation weather data from a reliable source. Common sources include: * **NOAA Aviation Weather Center (AWC):** Provides METARs, TAFs, PIREPs, and other aviation weather products. Often accessed via their XML/text feeds. * **Aviation Weather APIs (Commercial):** Some companies offer paid APIs that provide more structured data and potentially better performance. Examples include Aviation Edge, CheckWX, etc. 2. **Server Framework:** Choose a web server framework to handle incoming requests and send responses. Popular choices include: * **Flask (Python):** Lightweight and easy to learn. Good for simple APIs. * **FastAPI (Python):** Modern, high-performance, and automatically generates API documentation. * **Node.js (JavaScript):** If you prefer JavaScript. Express.js is a common framework. 3. **Data Parsing and Storage (Optional):** * **Parsing:** The data from the source (e.g., XML from NOAA) needs to be parsed into a usable format (e.g., Python dictionaries or objects). * **Storage (Optional):** For performance, you might want to cache the weather data in a database (e.g., Redis, PostgreSQL) or in memory. This avoids hitting the external API too frequently. 4. **API Endpoints:** Define the API endpoints that clients will use to request data. For example: * `/metar/{icao}`: Get the METAR for a specific airport (ICAO code). * `/taf/{icao}`: Get the TAF for a specific airport. * `/airports/search?q={query}`: Search for airports by name or ICAO code. 5. **Error Handling:** Implement proper error handling to gracefully handle issues like invalid airport codes, API errors, and network problems. 6. **Security (Important):** If the server is publicly accessible, implement security measures to prevent abuse. This might include rate limiting, authentication, and authorization. **Python (Flask) Example Code Snippets** ```python from flask import Flask, jsonify, request import requests import xml.etree.ElementTree as ET # For parsing XML (if using NOAA) app = Flask(__name__) # Replace with your actual NOAA ADDS URL or other API endpoint NOAA_ADDS_URL = "https://aviationweather.gov/adds/dataserver/.......your_query_here......." # Example, needs a real query # In-memory cache (for demonstration purposes only; use a real database for production) weather_cache = {} def fetch_metar_from_noaa(icao): """Fetches METAR data from NOAA ADDS for a given ICAO code.""" try: # Construct the NOAA ADDS query (example, adjust as needed) query = f"?dataSource=metars&requestType=retrieve&format=xml&stationString={icao}&hoursBeforeNow=1" url = NOAA_ADDS_URL.replace(".......your_query_here.......", query) response = requests.get(url) response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx) xml_data = response.text root = ET.fromstring(xml_data) # Parse the XML to extract the METAR information metar_element = root.find(".//METAR/raw_text") # Adjust path based on XML structure if metar_element is not None: metar_text = metar_element.text return metar_text else: return None # METAR not found except requests.exceptions.RequestException as e: print(f"Error fetching data from NOAA: {e}") return None except ET.ParseError as e: print(f"Error parsing XML: {e}") return None @app.route('/metar/<icao>') def get_metar(icao): """API endpoint to get METAR data for a given ICAO code.""" icao = icao.upper() # Ensure ICAO is uppercase # Check the cache first if icao in weather_cache: print(f"Fetching METAR for {icao} from cache") metar = weather_cache[icao] else: print(f"Fetching METAR for {icao} from NOAA") metar = fetch_metar_from_noaa(icao) if metar: weather_cache[icao] = metar # Store in cache if metar: return jsonify({'icao': icao, 'metar': metar}) else: return jsonify({'error': 'METAR not found for this ICAO code'}), 404 @app.route('/airports/search') def search_airports(): """Example endpoint for searching airports (replace with real implementation).""" query = request.args.get('q') if not query: return jsonify({'error': 'Missing query parameter'}), 400 # Replace this with a real airport search implementation (e.g., from a database) # This is just a placeholder if query.lower() == "jfk": results = [{"icao": "KJFK", "name": "John F. Kennedy International Airport"}] elif query.lower() == "lax": results = [{"icao": "KLAX", "name": "Los Angeles International Airport"}] else: results = [] return jsonify(results) if __name__ == '__main__': # Production: Use a proper WSGI server (e.g., gunicorn, uWSGI) app.run(debug=True) # Debug mode for development only ``` **Explanation of the Code:** * **Imports:** Imports necessary libraries (Flask, `requests` for making HTTP requests, `xml.etree.ElementTree` for parsing XML). * **`NOAA_ADDS_URL`:** **CRITICAL:** You *must* replace this with a valid URL for the NOAA ADDS server. The example is just a placeholder. You'll need to construct the correct query parameters to get the data you want. Refer to the NOAA ADDS documentation. * **`weather_cache`:** A simple in-memory dictionary to cache weather data. This is for demonstration only. In a real application, use a database like Redis. * **`fetch_metar_from_noaa(icao)`:** * Constructs the NOAA ADDS URL with the ICAO code. * Uses `requests` to fetch the data. * Parses the XML response using `xml.etree.ElementTree`. * Extracts the METAR text from the XML. **Important:** The XML structure can be complex. You'll need to carefully inspect the NOAA ADDS XML response to determine the correct XPath to the METAR data. * Handles potential errors (network errors, XML parsing errors). * **`get_metar(icao)`:** * The API endpoint for `/metar/{icao}`. * Checks the cache first. * If not in the cache, fetches the data from NOAA. * Returns the METAR data as a JSON response. * Returns a 404 error if the METAR is not found. * **`search_airports()`:** A placeholder for an airport search endpoint. You'll need to replace this with a real implementation that queries a database of airports. * **`app.run(debug=True)`:** Starts the Flask development server. **Important:** Do not use `debug=True` in a production environment. Use a proper WSGI server (e.g., gunicorn, uWSGI). **To Run This Example (After Replacing the Placeholder URL):** 1. **Install Flask and Requests:** ```bash pip install flask requests ``` 2. **Save the code:** Save the code as a Python file (e.g., `aviation_server.py`). 3. **Run the server:** ```bash python aviation_server.py ``` 4. **Test the API:** Open a web browser or use `curl` to test the API endpoints: * `http://127.0.0.1:5000/metar/KJFK` (Replace `KJFK` with a valid ICAO code) * `http://127.0.0.1:5000/airports/search?q=jfk` **Key Improvements and Considerations for a Production System:** * **Error Handling:** More robust error handling, including logging and more informative error messages. * **Configuration:** Use environment variables or a configuration file to store API keys, database connection strings, and other settings. * **Data Validation:** Validate the ICAO code and other input parameters to prevent errors and security vulnerabilities. * **Rate Limiting:** Implement rate limiting to prevent abuse of the API. * **Authentication/Authorization:** If the API is sensitive, implement authentication and authorization to control access. * **Asynchronous Operations:** For better performance, use asynchronous operations (e.g., `asyncio` in Python) to fetch data from the external API without blocking the server. * **Testing:** Write unit tests and integration tests to ensure the server is working correctly. * **Deployment:** Deploy the server to a production environment (e.g., AWS, Google Cloud, Azure) using a proper WSGI server (e.g., gunicorn, uWSGI). * **Monitoring:** Monitor the server's performance and error rates. * **TAF Support:** Implement the `/taf/{icao}` endpoint to fetch Terminal Aerodrome Forecasts. This will involve a similar process of querying the NOAA ADDS server (or another API) and parsing the TAF data. * **Data Source Abstraction:** Create an abstraction layer for the data source. This will make it easier to switch to a different API in the future. **Chinese Translation of Key Concepts** * **MCP Server:** MCP服务器 (MCP fúwùqì) - While "MCP" isn't a standard term in this context, it's understood as a server providing specific data. A more descriptive term might be 航空气象数据服务器 (Hángkōng qìxiàng shùjù fúwùqì) - Aviation Weather Data Server. * **Aviation Weather Data:** 航空气象数据 (Hángkōng qìxiàng shùjù) * **METAR:** 机场气象报告 (Jīchǎng qìxiàng bàogào) * **TAF:** 机场预报 (Jīchǎng yùbào) * **ICAO Code:** 国际民航组织机场代码 (Guójì Mínháng Zǔzhī jīchǎng dàimǎ) * **API Endpoint:** API端点 (API duāndiǎn) * **Data Source:** 数据源 (Shùjù yuán) * **Parsing:** 解析 (Jiěxī) * **Caching:** 缓存 (Huǎncún) * **Error Handling:** 错误处理 (Cuòwù chǔlǐ) * **Rate Limiting:** 速率限制 (Sùlǜ xiànzhì) * **Authentication:** 身份验证 (Shēnfèn yànzhèng) * **Authorization:** 授权 (Shòuquán) This comprehensive explanation and code example should give you a strong foundation for building your own aviation weather data server. Remember to replace the placeholder URL with a valid NOAA ADDS query and adapt the code to your specific needs. Good luck!