发现优秀的 MCP 服务器

Powerdrill

一个 MCP 服务器，提供与 Powerdrill (https://powerdrill.ai/) 数据集交互的工具，从而实现智能 AI 数据分析和洞察。

r2d2

用于 Dune Analytics 的开源 MCP 服务器

Alpha Vantage MCP

一个用于访问 Alpha Vantage 股票和金融数据的模型上下文协议 (MCP) 服务器。

Mcp_mlb_statsapi

这个项目提供了一个 MCP (多通道管道) 服务器，它作为 MLB Stats API 的包装器。它允许你轻松访问和处理各种 MLB 数据点，包括赛程、比赛结果和球队信息。这个服务器专为在 MCP 框架内高效地检索和处理数据而设计。

mcpserveraitools

刚刚发布了一个“模型上下文协议”（MCP）服务器，基本上就是JARVIS，但更有个性，而且没有英国口音！ 等机器人起义发生时，我肯定会在他们的“处理过这个人类的奇怪问题”名单上。

habitat

这是一个协作组件的集合，它们共同作用，可以方便地在本地和网络上管理、开发、使用和迁移 MCP 服务器。

Arithmo MCP Server pypi Package

PyPI 包 (PyPI bāo)

agoda-review-mcp

寻找Agoda酒店评价的MCP服务器

Crypto Portfolio MCP

一个用于追踪和管理加密货币投资组合配置的 MCP 服务器。

MCP Server for Paper Analytical Devices (PAD)

用于纸质分析设备 (PAD) 的 Python MCP 服务器

DVID MCP Server

用于对 DVID 进行主要只读访问的 MCP 服务器

ORKL MCP Server

ORKL 威胁情报库的 MCP 服务器

sheet-music-mcp

用于乐谱渲染的 MCP 服务器

cognee-mcp-server

镜子 (jìng zi)

PromptFuzzer-MCP

使用 Garak LLM 漏洞扫描器的 MCP 服务器

mcp-weather

好的，以下是一個給 AI Agent 使用的 MCP (Message Communication Protocol) Server 範例，用來取得美國各州的天氣預報與警示資訊。 這個範例著重於概念的清晰，實際部署可能需要根據您的具體需求進行調整。 **概念概述:** * **MCP Server:** 負責接收來自 AI Agent 的請求，處理請求，並將結果返回給 AI Agent。 * **AI Agent:** 發送請求到 MCP Server，並解析返回的結果。 * **天氣資料來源:** 使用外部 API (例如 NOAA, OpenWeatherMap) 來獲取天氣資訊。 * **資料格式:** 使用 JSON 作為請求和回應的資料格式，方便 AI Agent 解析。 **MCP Server (Python 範例 - 使用 Flask):** ```python from flask import Flask, request, jsonify import requests import os app = Flask(__name__) # 替換成你的 API 金鑰 (從 NOAA, OpenWeatherMap 等取得) API_KEY = os.environ.get("WEATHER_API_KEY") # 建議使用環境變數 # 預設天氣資料來源 (NOAA) WEATHER_API_URL = "https://api.weather.gov/alerts/active?area={state}" @app.route('/weather', methods=['POST']) def get_weather(): """ 接收來自 AI Agent 的請求，取得指定州的天氣預報和警示資訊。 """ try: data = request.get_json() state = data.get('state') if not state: return jsonify({'error': 'State is required'}), 400 # 呼叫天氣 API url = WEATHER_API_URL.format(state=state.upper()) # NOAA 需要大寫州代碼 response = requests.get(url) if response.status_code == 200: weather_data = response.json() return jsonify(weather_data), 200 else: return jsonify({'error': f'Weather API error: {response.status_code}'}), 500 except Exception as e: print(f"Error: {e}") return jsonify({'error': str(e)}), 500 if __name__ == '__main__': app.run(debug=True, host='0.0.0.0', port=5000) ``` **AI Agent (Python 範例):** ```python import requests import json MCP_SERVER_URL = "http://localhost:5000/weather" # 替換成你的 MCP Server URL def get_state_weather(state): """ 向 MCP Server 發送請求，取得指定州的天氣資訊。 """ try: payload = {'state': state} headers = {'Content-type': 'application/json'} response = requests.post(MCP_SERVER_URL, data=json.dumps(payload), headers=headers) if response.status_code == 200: weather_data = response.json() return weather_data else: print(f"Error: MCP Server error: {response.status_code}") return None except Exception as e: print(f"Error: {e}") return None # 範例用法 state = "CA" # 加州 weather_info = get_state_weather(state) if weather_info: print(f"加州 ({state}) 天氣資訊:") print(json.dumps(weather_info, indent=4, ensure_ascii=False)) # 輸出美觀的 JSON else: print("無法取得天氣資訊。") ``` **說明:** * **MCP Server (Flask):** * 使用 Flask 建立一個簡單的 Web 服務。 * `/weather` 端點接收 POST 請求，請求體包含一個 JSON 物件，其中包含 `state` 欄位 (例如: `{"state": "CA"}`). * 使用 `requests` 庫呼叫外部天氣 API (NOAA 在此範例中)。 * 將天氣 API 的回應以 JSON 格式返回給 AI Agent。 * 錯誤處理：包含基本的錯誤處理，例如檢查 `state` 是否存在，以及處理天氣 API 的錯誤。 * **重要:** 請務必將 `API_KEY` 儲存在環境變數中，而不是直接寫在程式碼中，以確保安全性。 * **AI Agent:** * 使用 `requests` 庫向 MCP Server 發送 POST 請求。 * 將 `state` 作為 JSON payload 發送。 * 解析 MCP Server 返回的 JSON 回應。 * 錯誤處理：包含基本的錯誤處理，例如檢查 MCP Server 的回應狀態碼。 * **JSON 格式:** 請求和回應都使用 JSON 格式，方便 AI Agent 解析和處理。 * **NOAA API:** 此範例使用 NOAA 的 API，但您可以根據需要替換成其他天氣 API。 請注意，不同的 API 可能需要不同的參數和金鑰。 * **錯誤處理:** 範例中包含基本的錯誤處理，但您可能需要根據您的需求添加更完善的錯誤處理機制。 * **安全性:** 在實際部署中，請務必考慮安全性問題，例如驗證 AI Agent 的身份，以及保護 API 金鑰。 **如何執行:** 1. **安裝必要的套件:** ```bash pip install flask requests ``` 2. **設定環境變數:** ```bash export WEATHER_API_KEY="YOUR_API_KEY" # 替換成你的 API 金鑰 ``` 3. **執行 MCP Server:** ```bash python your_mcp_server_file.py ``` 4. **執行 AI Agent:** ```bash python your_ai_agent_file.py ``` **中文翻譯 (概念):** 這個範例展示了一個給 AI 代理使用的 MCP (訊息通訊協定) 伺服器，用於獲取美國各州的天氣預報和警報資訊。 * **MCP 伺服器:** 負責接收來自 AI 代理的請求，處理這些請求，並將結果返回給 AI 代理。 * **AI 代理:** 向 MCP 伺服器發送請求，並解析返回的結果。 * **天氣資料來源:** 使用外部 API (例如 NOAA, OpenWeatherMap) 來獲取天氣資訊。 * **資料格式:** 使用 JSON 作為請求和回應的資料格式，方便 AI 代理解析。 **中文翻譯 (程式碼註解):** 程式碼中的註解已經是中文，所以不需要額外翻譯。 **重要注意事項:** * **API 金鑰:** 請務必使用您自己的 API 金鑰，並將其儲存在環境變數中。 * **錯誤處理:** 根據您的需求，添加更完善的錯誤處理機制。 * **安全性:** 在實際部署中，請考慮安全性問題。 * **API 限制:** 不同的天氣 API 可能有不同的使用限制 (例如請求頻率限制)。 請仔細閱讀 API 的文件。 * **資料格式:** 不同的天氣 API 返回的資料格式可能不同。 您可能需要修改程式碼來解析不同的資料格式。 這個範例提供了一個基本的框架。 您可以根據您的具體需求進行修改和擴展。 例如，您可以添加更多的功能，例如支持不同的天氣 API，或者提供更詳細的天氣資訊。

MCP-OpenLLM

LangChain 封装器，用于将 MCP 服务器与 transformers 库中不同的开源大型语言模型无缝集成。

ChargeNow MCP Server

用于查找 ChargeNow 电动汽车充电站的 MCP 服务器。

MCP客户端应用

移动应用程序连接到 Cloudflare MCP 服务器。 (Yídòng yìngyòng chéngxù liánjiē dào Cloudflare MCP fúwùqì.)

SEC EDGAR MCP

用于美国证券交易委员会 EDGAR 系统的模型上下文协议 (MCP) 服务器

MCPサーバの作り方 & 使い方

MCP Server/Client 指的是 **Minecraft Protocol Server/Client**，即 **Minecraft 协议服务器/客户端**。 更具体地说： * **Minecraft Protocol (MCP)**: 这是 Minecraft 客户端和服务器之间通信所使用的协议。它定义了客户端和服务器如何交换数据，例如玩家的位置、方块的变化、聊天消息等等。 它是一套规则和格式，确保双方能够理解彼此发送的信息。 * **MCP Server (Minecraft Protocol Server)**: 这是一个实现了 Minecraft 协议的服务器。 官方的 Minecraft 服务器就是一个 MCP 服务器。 但是，也存在许多其他的 MCP 服务器实现，例如用于创建自定义服务器或代理服务器。 它们负责接收来自客户端的请求，处理这些请求，并将响应发送回客户端。 * **MCP Client (Minecraft Protocol Client)**: 这是一个实现了 Minecraft 协议的客户端。 官方的 Minecraft 游戏客户端就是一个 MCP 客户端。 它负责连接到 MCP 服务器，发送请求（例如移动、挖掘、聊天），并接收来自服务器的响应，然后将这些响应呈现给玩家。 同样，也存在非官方的 MCP 客户端，例如用于自动化任务或测试。 **简单来说：** 想象一下，你在餐厅点餐。 * **MCP 协议** 就像餐厅的菜单，规定了你可以点什么菜，以及如何点菜（例如，你需要说出菜名和数量）。 * **MCP 服务器** 就像餐厅的服务员，负责接收你的订单，将订单交给厨房，然后将做好的菜端给你。 * **MCP 客户端** 就像你，负责阅读菜单，点菜，然后享用美食。 因此，MCP Server/Client 描述了 Minecraft 客户端和服务器之间基于 Minecraft 协议的通信关系。

Phalcon MCP Server

集成了 BlockSec 的 MCP 服务器 (Jí chéng le BlockSec de MCP fúwùqì) This translates to: * **集成 (Jí chéng):** Integrated / Integrates * **BlockSec 的 (BlockSec de):** BlockSec's / of BlockSec * **MCP 服务器 (MCP fúwùqì):** MCP Server Therefore, the whole phrase means "MCP server that integrates with BlockSec".

Spotify-Agent

Okay, this is a complex project, and I can't provide you with a complete, ready-to-run MCP (Minecraft Protocol) server implementation here. Building a full MCP server with Spotify, Last.fm, and internet integration for music recommendations requires significant coding effort and knowledge of several technologies. However, I can give you a detailed outline, code snippets (in Python, as it's commonly used for these tasks), and guidance to get you started. **I. Project Overview** The goal is to create an MCP server plugin (or a standalone server that interacts with a Minecraft server) that: 1. **Connects to Spotify and Last.fm APIs:** Retrieves user listening history, track information, artist information, and similar artist data. 2. **Interacts with the Internet (e.g., for Lyrics):** Fetches lyrics for songs. 3. **Stores and Processes Music Data:** Creates a database or data structure to store user preferences, track information, and recommendation data. 4. **Provides Minecraft Commands:** Allows players to request recommendations, view their listening history, and interact with the music system within the game. 5. **Delivers Recommendations:** Generates music recommendations based on user data and similarity algorithms. **II. Technology Stack** * **Minecraft Server:** You'll need a Minecraft server (e.g., Spigot, Paper, Fabric). Choose one you're comfortable with. * **MCP Server Library/Framework:** Choose a library that simplifies MCP server development. Popular options include: * **Python:** `mcstatus`, `nbt`, `minecraft-protocol` (for lower-level control) * **Java:** Spigot API, Paper API (if you're building a plugin) * **Programming Language:** Python or Java are good choices. Python is often preferred for scripting and API interactions. * **Spotify API:** Use the Spotify Web API (requires a Spotify Developer account). * **Last.fm API:** Use the Last.fm API (requires a Last.fm API key). * **Lyrics API (Optional):** Consider using a lyrics API like Lyrics.ovh or Musixmatch API. * **Database (Optional):** Consider using a database (e.g., SQLite, MySQL, PostgreSQL) to store user data and music information. SQLite is a good choice for smaller projects. * **Recommendation Algorithm:** Implement a recommendation algorithm (e.g., collaborative filtering, content-based filtering, hybrid approach). **III. Detailed Steps and Code Snippets (Python Example)** **1. Setting up the MCP Server (Python)** ```python import socket import struct import json import threading # Configuration HOST = 'localhost' PORT = 25565 # Default Minecraft port def handle_client(conn, addr): print(f"Connected by {addr}") try: # Handshake data = conn.recv(256) packet_length = struct.unpack('>i', data[:4])[0] packet_id = struct.unpack('>b', data[4:5])[0] if packet_id == 0x00: # Handshake packet handshake_data = json.loads(data[5:].decode('utf-8')) print(f"Handshake data: {handshake_data}") # Status Request data = conn.recv(256) packet_length = struct.unpack('>i', data[:4])[0] packet_id = struct.unpack('>b', data[4:5])[0] if packet_id == 0x00: # Status Request # Respond with server status status = { "version": { "name": "My Music Server", "protocol": 754 # Example protocol version }, "players": { "max": 100, "online": 0, "sample": [] }, "description": { "text": "A server for music recommendations!" } } status_json = json.dumps(status) status_bytes = status_json.encode('utf-8') status_length = len(status_bytes) response = struct.pack('>i', status_length + 1) + struct.pack('>b', 0x00) + status_bytes conn.sendall(response) # Ping Request data = conn.recv(256) packet_length = struct.unpack('>i', data[:4])[0] packet_id = struct.unpack('>b', data[4:5])[0] if packet_id == 0x01: # Ping Request ping_payload = data[5:] response = struct.pack('>i', len(ping_payload) + 1) + struct.pack('>b', 0x01) + ping_payload conn.sendall(response) except Exception as e: print(f"Error handling client: {e}") finally: conn.close() print(f"Connection closed with {addr}") def start_server(): server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) # Allow reuse of the address server_socket.bind((HOST, PORT)) server_socket.listen(5) print(f"Server listening on {HOST}:{PORT}") while True: conn, addr = server_socket.accept() client_thread = threading.Thread(target=handle_client, args=(conn, addr)) client_thread.start() if __name__ == "__main__": start_server() ``` **Explanation:** * **`socket`:** Creates a socket for network communication. * **`struct`:** Packs and unpacks data in binary format (required for the Minecraft protocol). * **`json`:** Handles JSON data for the server status. * **`threading`:** Allows handling multiple client connections concurrently. * **`handle_client`:** This function handles the communication with a single Minecraft client. It receives the handshake, status request, and ping request, and sends back the appropriate responses. * **`start_server`:** Sets up the server socket and listens for incoming connections. **Important:** This is a *very* basic MCP server. It only handles the handshake, status request, and ping request. You'll need to implement the full Minecraft protocol to handle player login, chat messages, commands, and other game events. **2. Integrating with Spotify and Last.fm APIs** ```python import spotipy from spotipy.oauth2 import SpotifyClientCredentials import pylast # Spotify API Credentials (replace with your own) SPOTIPY_CLIENT_ID = "YOUR_SPOTIFY_CLIENT_ID" SPOTIPY_CLIENT_SECRET = "YOUR_SPOTIFY_CLIENT_SECRET" # Last.fm API Credentials (replace with your own) LASTFM_API_KEY = "YOUR_LASTFM_API_KEY" LASTFM_API_SECRET = "YOUR_LASTFM_API_SECRET" # Initialize Spotify client client_credentials_manager = SpotifyClientCredentials(client_id=SPOTIPY_CLIENT_ID, client_secret=SPOTIPY_CLIENT_SECRET) sp = spotipy.Spotify(client_credentials_manager=client_credentials_manager) # Initialize Last.fm network network = pylast.LastFMNetwork( api_key=LASTFM_API_KEY, api_secret=LASTFM_API_SECRET, username="YOUR_LASTFM_USERNAME" # Replace with a Last.fm username for testing ) def get_spotify_track_info(track_name, artist_name): """Searches Spotify for a track and returns track information.""" query = f"track:{track_name} artist:{artist_name}" results = sp.search(q=query, type='track', limit=1) if results['tracks']['items']: track = results['tracks']['items'][0] return { 'id': track['id'], 'name': track['name'], 'artist': track['artists'][0]['name'], 'album': track['album']['name'], 'popularity': track['popularity'], 'uri': track['uri'] } else: return None def get_lastfm_recent_tracks(username): """Gets the recent tracks played by a Last.fm user.""" user = network.get_user(username) recent_tracks = user.get_recent_tracks(limit=10) # Get the last 10 tracks tracks = [] for item in recent_tracks: track = item.track tracks.append({ 'artist': track.artist.name, 'title': track.title }) return tracks # Example usage if __name__ == "__main__": # Spotify Example track_info = get_spotify_track_info("Bohemian Rhapsody", "Queen") if track_info: print("Spotify Track Info:", track_info) else: print("Track not found on Spotify.") # Last.fm Example recent_tracks = get_lastfm_recent_tracks("YOUR_LASTFM_USERNAME") # Replace with your Last.fm username print("\nLast.fm Recent Tracks:") for track in recent_tracks: print(f"- {track['artist']} - {track['title']}") ``` **Explanation:** * **`spotipy`:** The Spotify API library. You'll need to install it: `pip install spotipy` * **`pylast`:** The Last.fm API library. You'll need to install it: `pip install pylast` * **API Credentials:** You *must* replace the placeholder credentials with your own API keys and secrets from the Spotify Developer Dashboard and the Last.fm API page. * **`get_spotify_track_info`:** Searches Spotify for a track and returns information like ID, name, artist, album, and popularity. * **`get_lastfm_recent_tracks`:** Gets the recent tracks played by a Last.fm user. **3. Handling Minecraft Commands** You'll need to integrate your music functionality with Minecraft commands. The specific way to do this depends on the MCP server library you're using. Here's a general outline: * **Command Registration:** Register a new command (e.g., `/musicrecommend`, `/mylistenhistory`). * **Command Parsing:** Parse the arguments provided with the command. * **Command Execution:** Execute the appropriate music-related function based on the command and arguments. * **Response to Player:** Send a message back to the player in the Minecraft chat window with the results (e.g., the recommended songs, their listening history). **Example (Conceptual - using a hypothetical MCP library):** ```python # Hypothetical MCP library functions def register_command(command_name, command_handler): # Registers a command with the server pass def send_message_to_player(player_name, message): # Sends a message to a player in the game pass def handle_music_recommend_command(player_name, args): # Get the player's Last.fm username (from a database or configuration) lastfm_username = get_player_lastfm_username(player_name) if not lastfm_username: send_message_to_player(player_name, "Please link your Last.fm account first.") return # Get the player's recent tracks from Last.fm recent_tracks = get_lastfm_recent_tracks(lastfm_username) # Generate recommendations (implementation details omitted) recommendations = generate_recommendations(recent_tracks) # Format the recommendations into a message message = "Recommended Songs:\n" for track in recommendations: message += f"- {track['artist']} - {track['title']}\n" # Send the recommendations to the player send_message_to_player(player_name, message) def get_player_lastfm_username(player_name): # This function would retrieve the Last.fm username associated with the player # from a database or configuration file. # For example: # player_data = database.get_player_data(player_name) # return player_data.get('lastfm_username') return "YOUR_LASTFM_USERNAME" # Replace with your Last.fm username for testing # Register the command register_command("musicrecommend", handle_music_recommend_command) ``` **4. Recommendation Algorithm** This is the most complex part. Here are some approaches: * **Collaborative Filtering:** Find users with similar listening habits and recommend songs they like that the current user hasn't heard. Requires a database of user listening data. * **Content-Based Filtering:** Analyze the characteristics of the songs the user likes (e.g., genre, tempo, mood) and recommend songs with similar characteristics. You can use the Spotify API to get track features. * **Hybrid Approach:** Combine collaborative and content-based filtering for better results. **Example (Simplified Content-Based Filtering):** ```python def generate_recommendations(recent_tracks): """Generates recommendations based on recent tracks.""" recommendations = [] for track in recent_tracks: # Get Spotify track info track_info = get_spotify_track_info(track['title'], track['artist']) if track_info: # Get similar artists from Spotify artist_id = sp.artist(track_info['uri'].split(':')[2])['artists'][0]['id'] related_artists = sp.artist_related_artists(artist_id)['artists'] # Get top tracks from related artists for artist in related_artists[:3]: # Limit to 3 related artists top_tracks = sp.artist_top_tracks(artist['id'])['tracks'][:2] # Limit to 2 tracks for top_track in top_tracks: recommendations.append({ 'artist': top_track['artists'][0]['name'], 'title': top_track['name'] }) return recommendations ``` **5. Database (Optional)** If you want to store user data (e.g., Last.fm usernames, listening history), you'll need a database. SQLite is a good choice for smaller projects. ```python import sqlite3 # Database setup conn = sqlite3.connect('music_server.db') cursor = conn.cursor() # Create a table to store player data cursor.execute(''' CREATE TABLE IF NOT EXISTS players ( player_name TEXT PRIMARY KEY, lastfm_username TEXT ) ''') conn.commit() def set_player_lastfm_username(player_name, lastfm_username): """Sets the Last.fm username for a player in the database.""" cursor.execute("INSERT OR REPLACE INTO players (player_name, lastfm_username) VALUES (?, ?)", (player_name, lastfm_username)) conn.commit() def get_player_lastfm_username(player_name): """Gets the Last.fm username for a player from the database.""" cursor.execute("SELECT lastfm_username FROM players WHERE player_name = ?", (player_name,)) result = cursor.fetchone() if result: return result[0] else: return None # Example usage if __name__ == "__main__": set_player_lastfm_username("Player123", "YOUR_LASTFM_USERNAME") # Replace with your Last.fm username username = get_player_lastfm_username("Player123") print(f"Last.fm username for Player123: {username}") conn.close() ``` **IV. Key Considerations and Challenges** * **Minecraft Protocol:** Understanding the Minecraft protocol is crucial for building a functional MCP server. It's complex and constantly evolving. * **API Rate Limits:** The Spotify and Last.fm APIs have rate limits. You need to handle these limits gracefully to avoid being blocked. Implement caching and throttling. * **User Authentication:** You'll need a way for players to link their Spotify/Last.fm accounts to their Minecraft accounts. This typically involves OAuth 2.0. * **Data Storage:** Choosing the right data storage solution (database, file system, etc.) depends on the scale of your project. * **Performance:** Generating recommendations can be computationally expensive. Optimize your code and consider using background threads or asynchronous tasks. * **Error Handling:** Implement robust error handling to deal with API errors, network issues, and invalid user input. * **Security:** Protect API keys and user data. Don't store sensitive information in plain text. * **Scalability:** If you plan to support a large number of users, you'll need to design your system to be scalable. **V. Next Steps** 1. **Choose an MCP Server Library:** Select a library that suits your needs and programming language preference. 2. **Implement the Basic MCP Server:** Get a basic MCP server running that can handle the handshake, status request, and ping request. 3. **Integrate with the Spotify and Last.fm APIs:** Implement the functions to retrieve user listening history and track information. 4. **Implement Command Handling:** Add the ability to register and handle Minecraft commands. 5. **Implement a Recommendation Algorithm:** Start with a simple algorithm and gradually improve it. 6. **Add a Database (Optional):** If you need to store user data, set up a database. 7. **Test Thoroughly:** Test your server with multiple Minecraft clients and different scenarios. **VI. Chinese Translation of Key Terms** Here's a translation of some key terms that might be helpful: * **MCP Server:** MCP服务器 (MCP fúwùqì) * **Spotify API:** Spotify API (Spotify API) (Note: API is often used directly in Chinese) * **Last.fm API:** Last.fm API (Last.fm API) * **Recommendation Algorithm:** 推荐算法 (tuījiàn suànfǎ) * **Minecraft Command:** Minecraft 命令 (Minecraft mìnglìng) * **Listening History:** 听歌历史 (tīng gē lìshǐ) * **API Key:** API 密钥 (API mìyào) * **Database:** 数据库 (shùjùkù) * **User Authentication:** 用户认证 (yònghù rènzhèng) * **Rate Limit:** 速率限制 (sùlǜ xiànzhì) or 频率限制 (pínlǜ xiànzhì) * **Collaborative Filtering:** 协同过滤 (xiétóng guòlǜ) * **Content-Based Filtering:** 基于内容的过滤 (jī yú nèiróng de guòlǜ) This is a substantial project, but by breaking it down into smaller steps and using the resources and guidance provided, you can make progress towards building your music recommendation MCP server. Good luck!

ifly-spark-agent-mcp

这是一个使用 MCP Server 调用讯飞星火认知大模型平台任务链的简单示例。

Sequential Thinking MCP Server

一个 MCP 服务器，它通过构建思维过程并自动将每个会话记录到 Recall，从而实现动态的、反思性的问题解决。

Bing Search MCP Server

一个模型上下文协议服务器，它与微软必应搜索 API 集成，允许 AI 助手执行网页、新闻和图像搜索。

StrongApps_MCPE_servers

镜子 (jìng zi)