发现优秀的 MCP 服务器

Crypto Tracker MCP Server

Connects AI agents to real-time cryptocurrency market data from CoinGecko API, enabling price lookups, coin details, market rankings, search, and trending crypto queries through natural language.

YouTube Transcript MCP

Enables AI models to extract transcripts from YouTube videos in multiple languages with zero local setup. It supports all YouTube URL formats and features smart caching via Cloudflare Workers for fast responses.

K8s MCP Server

K8s-mcp-server 是一个模型上下文协议 (MCP) 服务器，它使像 Claude 这样的 AI 助手能够安全地执行 Kubernetes 命令。它在语言模型和必要的 Kubernetes CLI 工具（包括 kubectl、helm、istioctl 和 argocd）之间提供了一座桥梁，允许 AI 系统协助集群管理、故障排除和部署。

MCP Fly Deployer

提供 Docker 文件的 MCP 服务器，用于将基于 stdio 的 MCP 服务器部署在 Fly.IO 等平台上。

Riddles By Api Ninjas MCP Server

Enables access to the API Ninjas Riddles API to retrieve random riddles. Users can request between 1-20 riddles at a time through a simple interface.

Dooray MCP Server

Enables interaction with Dooray's task and calendar management system, allowing users to filter and list tasks, retrieve details, and manage task comments. It provides a set of tools for seamless integration with MCP-compatible clients like Claude Desktop and Cursor.

Google Calendar MCP Server

Enables interaction with Google Calendar services through the Model Context Protocol using a Service Account. It allows MCP-compatible clients to manage calendar events and integrate calendar data via the Google Calendar API.

MCP Server Tester

一个使用来自 Smithery 的安装代码来测试 MCP 服务器的 Web 应用程序。

Remote MCP Server (Authless)

A template for deploying MCP servers without authentication on Cloudflare Workers. Enables custom tool creation and integration with Claude Desktop and AI Playground through Server-Sent Events.

InfraNodus MCP Server

Enables advanced text network analysis and knowledge graph generation using graph theory algorithms to identify topics, detect content gaps, and extract structured insights from unstructured text.

MCP Todo List Manager

Enables natural language todo list management through Claude Desktop with YAML-based persistence. Supports creating, completing, deleting, and listing todo items with automatic timestamp tracking and secure file permissions.

Lotus MCP

Enables creation of reusable browser automation skills through demonstration by recording user actions in a browser while narrating, then converting those workflows into executable skills that can be invoked through natural language.

e代驾 MCP Server

A service that provides complete driver-for-hire functionality based on e代驾 open APIs, enabling users to order drivers, calculate pricing, create and track orders.

Hex MCP Server

六角包版本的 MCP 服务器

Banxico MCP Server

Enables access to Bank of Mexico (Banxico) economic data including real-time and historical USD/MXN exchange rates, inflation data, interest rates, and other financial indicators. Supports querying current rates, historical data with date ranges, and economic metadata through natural language.

MotaWord MCP Server

This MCP gives you full control over your translation projects from start to finish. You can log in anytime to see what stage your project is in — whether it’s being translated, reviewed, or completed. You don’t have to guess or follow up via email.

Docker MCP Server

Enables AI assistants like Claude to manage Docker containers, images, and Docker Compose deployments through the Model Context Protocol. Provides secure container lifecycle management, image operations, and multi-host Docker server connections.

Tanda Workforce MCP Server

Integrates Tanda Workforce API with AI assistants to manage employee schedules, timesheets, leave requests, clock in/out operations, and workforce analytics through natural language with OAuth2 authentication.

Local FAISS MCP Server

Provides local vector database functionality using FAISS for document ingestion, semantic search, and Retrieval-Augmented Generation (RAG) applications with persistent storage and customizable embedding models.

Prompt Cleaner MCP Server

Enables cleaning and sanitizing prompts through an LLM-powered tool that removes sensitive information, provides structured feedback with notes and risks, and normalizes prompt formatting. Supports configurable local or remote OpenAI-compatible APIs with automatic secret redaction.

README

Here are a few ways to interpret "Example MCP Server implements by Go" and their corresponding Chinese translations, along with some context: **1. Most Literal Translation (Focus on the words):** * **Chinese:** 使用 Go 实现的 MCP 服务器示例 * **Pinyin:** Shǐyòng Go shíxiàn de MCP fúwùqì shìlì * **Explanation:** This is a direct translation. It emphasizes that the example server is *implemented* using Go. **2. More Natural Translation (Focus on the meaning):** * **Chinese:** 一个用 Go 语言编写的 MCP 服务器示例 * **Pinyin:** Yī gè yòng Go yǔyán biānxiě de MCP fúwùqì shìlì * **Explanation:** This is a more common and natural way to say it in Chinese. It emphasizes that the server is *written* in Go. **3. If you want to emphasize the *purpose* of the example:** * **Chinese:** Go 语言实现的 MCP 服务器示例，用于演示... (add what it demonstrates) * **Pinyin:** Go yǔyán shíxiàn de MCP fúwùqì shìlì, yòng yú yǎnshì... * **Explanation:** This translates to "A Go language implemented MCP server example, used to demonstrate..." You would then fill in what the example is meant to demonstrate (e.g., basic functionality, specific features, etc.). **4. If you're looking for code examples (which is likely):** * **Chinese:** Go 语言 MCP 服务器示例代码 * **Pinyin:** Go yǔyán MCP fúwùqì shìlì dàimǎ * **Explanation:** This translates to "Go language MCP server example code." This is what you'd use if you're specifically looking for code snippets. **Key Vocabulary:** * **MCP:** MCP (usually left as is, as it's an acronym) * **Server:** 服务器 (fúwùqì) * **Example:** 示例 (shìlì) * **Implements/Implemented:** 实现 (shíxiàn) * **Go (programming language):** Go 语言 (Go yǔyán) * **Written (in a language):** 编写 (biānxiě) * **Code:** 代码 (dàimǎ) * **Used for demonstrating:** 用于演示 (yòng yú yǎnshì) **Which translation is best depends on the context.** If you're just stating a fact, option 2 is probably the most natural. If you're looking for code, option 4 is best. If you want to explain the purpose of the example, use option 3 and fill in the details.

NIX MCP Server

Enables AI-powered blockchain data queries and analysis through the Native Indexer (NIX) system. Supports querying blocks, transactions, account information, and network status across various blockchain networks.

Freshdesk MCP Server

Enables interaction with Freshdesk API v2 to manage support tickets, contacts, agents, companies, and conversations with built-in authentication, rate limiting, and error handling.

MCP demo (DeepSeek as Client's LLM)

Okay, I can help you outline the steps to run a minimal client-server demo using the DeepSeek API, focusing on the core concepts and providing example code snippets. Since I can't directly execute code or set up environments, I'll give you the instructions and code you'll need to adapt and run yourself. **Important Considerations Before You Start:** * **DeepSeek API Key:** You'll need a valid DeepSeek API key. Obtain one from the DeepSeek AI platform. Keep it secure and don't hardcode it directly into your scripts (use environment variables or configuration files). * **Python Environment:** I'll assume you're using Python. Make sure you have Python 3.7+ installed. * **Libraries:** You'll need the `requests` library for making HTTP requests to the DeepSeek API. Install it using `pip install requests`. You might also want `Flask` or `FastAPI` for a simple server. **Conceptual Overview** 1. **Client:** The client sends a request to the server. In this case, the request will contain a prompt that you want DeepSeek to complete. 2. **Server:** The server receives the request from the client, calls the DeepSeek API with the prompt, gets the response from DeepSeek, and sends the response back to the client. 3. **DeepSeek API:** This is the external service that performs the language model inference. **Step-by-Step Instructions and Code Examples** **1. Server (using Flask)** ```python # server.py from flask import Flask, request, jsonify import requests import os app = Flask(__name__) # Replace with your actual DeepSeek API key (ideally from an environment variable) DEEPSEEK_API_KEY = os.environ.get("DEEPSEEK_API_KEY") # Get from environment DEEPSEEK_API_URL = "https://api.deepseek.com/v1/chat/completions" # Replace if different @app.route('/generate', methods=['POST']) def generate_text(): try: data = request.get_json() prompt = data.get('prompt') if not prompt: return jsonify({'error': 'Prompt is required'}), 400 headers = { 'Content-Type': 'application/json', 'Authorization': f'Bearer {DEEPSEEK_API_KEY}' } payload = { "model": "deepseek-chat", # Or another DeepSeek model "messages": [{"role": "user", "content": prompt}], "max_tokens": 200, # Adjust as needed "temperature": 0.7 # Adjust as needed } response = requests.post(DEEPSEEK_API_URL, headers=headers, json=payload) response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx) deepseek_data = response.json() generated_text = deepseek_data['choices'][0]['message']['content'] return jsonify({'generated_text': generated_text}) except requests.exceptions.RequestException as e: print(f"API Request Error: {e}") return jsonify({'error': f'API Request Error: {e}'}), 500 except Exception as e: print(f"Server Error: {e}") return jsonify({'error': f'Server Error: {e}'}), 500 if __name__ == '__main__': app.run(debug=True, port=5000) # Or any port you prefer ``` **Explanation of `server.py`:** * **Imports:** Imports necessary libraries (Flask, requests, json, os). * **API Key:** Retrieves the DeepSeek API key from an environment variable. **Never hardcode your API key directly in the script!** * **Flask App:** Creates a Flask web application. * **`/generate` Route:** Defines a route that listens for POST requests at `/generate`. * **Request Handling:** * Extracts the `prompt` from the JSON request body. * Constructs the headers for the DeepSeek API request, including the `Authorization` header with your API key. * Creates the payload (JSON data) for the DeepSeek API request. This includes the model name, the prompt (formatted as a message), and other parameters like `max_tokens` and `temperature`. * Sends the request to the DeepSeek API using `requests.post()`. * Handles potential errors (e.g., network issues, invalid API key). * **Response Handling:** * Parses the JSON response from the DeepSeek API. * Extracts the generated text from the response. The exact structure of the response depends on the DeepSeek API. The code assumes a structure like `deepseek_data['choices'][0]['message']['content']`. **You might need to adjust this based on the actual DeepSeek API response format.** * Returns the generated text as a JSON response to the client. * **Error Handling:** Includes `try...except` blocks to catch potential errors during the API request and server processing. Returns error messages to the client. * **Running the App:** Starts the Flask development server. **2. Client (using Python)** ```python # client.py import requests import json SERVER_URL = "http://localhost:5000/generate" # Adjust if your server is running on a different address/port def generate_text(prompt): try: payload = {'prompt': prompt} headers = {'Content-Type': 'application/json'} response = requests.post(SERVER_URL, headers=headers, data=json.dumps(payload)) response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx) data = response.json() generated_text = data.get('generated_text') return generated_text except requests.exceptions.RequestException as e: print(f"Request Error: {e}") return None except Exception as e: print(f"Error: {e}") return None if __name__ == '__main__': user_prompt = "Write a short story about a cat who goes on an adventure." generated_text = generate_text(user_prompt) if generated_text: print("Generated Text:") print(generated_text) else: print("Failed to generate text.") ``` **Explanation of `client.py`:** * **Imports:** Imports the `requests` and `json` libraries. * **`SERVER_URL`:** Defines the URL of the server's `/generate` endpoint. Make sure this matches the address and port where your server is running. * **`generate_text(prompt)` Function:** * Takes a `prompt` as input. * Constructs the payload (JSON data) to send to the server. * Sets the `Content-Type` header to `application/json`. * Sends a POST request to the server using `requests.post()`. * Handles potential errors (e.g., network issues, server not available). * Parses the JSON response from the server. * Extracts the `generated_text` from the response. * Returns the generated text. * **Main Execution Block:** * Sets a sample `user_prompt`. * Calls the `generate_text()` function to get the generated text. * Prints the generated text to the console. **3. Running the Demo** 1. **Set the API Key:** Before running anything, set the `DEEPSEEK_API_KEY` environment variable. How you do this depends on your operating system: * **Linux/macOS:** ```bash export DEEPSEEK_API_KEY="YOUR_DEEPSEEK_API_KEY" ``` * **Windows (Command Prompt):** ```cmd set DEEPSEEK_API_KEY=YOUR_DEEPSEEK_API_KEY ``` * **Windows (PowerShell):** ```powershell $env:DEEPSEEK_API_KEY="YOUR_DEEPSEEK_API_KEY" ``` **Replace `YOUR_DEEPSEEK_API_KEY` with your actual API key.** 2. **Run the Server:** Open a terminal or command prompt, navigate to the directory where you saved `server.py`, and run: ```bash python server.py ``` The Flask development server will start, and you'll see output indicating that it's running. 3. **Run the Client:** Open another terminal or command prompt, navigate to the directory where you saved `client.py`, and run: ```bash python client.py ``` The client will send a request to the server, the server will call the DeepSeek API, and the generated text will be printed to the client's console. **Important Notes and Troubleshooting** * **API Key:** Double-check that your API key is correct and that you've set the environment variable properly. An incorrect API key will result in an authentication error. * **Network Connectivity:** Make sure your server has internet access to reach the DeepSeek API. * **Error Messages:** Carefully examine any error messages you receive. They often provide clues about what's going wrong. * **DeepSeek API Response Format:** The code assumes a specific format for the DeepSeek API response. If the API changes its response format, you'll need to update the code accordingly. Refer to the DeepSeek API documentation for the correct format. * **Rate Limits:** Be aware of the DeepSeek API's rate limits. If you send too many requests in a short period, you might get rate-limited. Implement error handling and potentially retry logic to deal with rate limits. * **Security:** For production environments, use a more robust web server (like Gunicorn or uWSGI) instead of the Flask development server. Also, consider using HTTPS for secure communication between the client and server. * **Model Selection:** The code uses `"deepseek-chat"` as the model. Check the DeepSeek API documentation for other available models and their capabilities. * **Prompt Engineering:** The quality of the generated text depends heavily on the prompt you provide. Experiment with different prompts to get the best results. **Simplified Chinese Translation of Key Phrases** Here are some key phrases translated into Simplified Chinese: * **Prompt:** 提示 (tíshì) * **Generated Text:** 生成的文本 (shēngchéng de wénběn) * **API Key:** API 密钥 (API mìyào) * **Server:** 服务器 (fúwùqì) * **Client:** 客户端 (kèhùduān) * **Error:** 错误 (cuòwù) * **Request:** 请求 (qǐngqiú) * **Response:** 响应 (xiǎngyìng) * **Authentication:** 身份验证 (shēnfèn yànzhèng) * **Rate Limit:** 速率限制 (sùlǜ xiànzhì) This detailed guide should help you get started with a basic DeepSeek API client-server demo. Remember to adapt the code to your specific needs and consult the DeepSeek API documentation for the most up-to-date information. Good luck!

Agent Progress Tracker MCP Server

Enables AI agents to track, search, and retrieve their progress across projects with persistent memory using SQLite storage and LLM-powered summarization. Supports logging completed work, searching previous entries, and retrieving context for multi-step or multi-agent workflows.

Oracle HCM Cloud MCP Server by CData

Oracle HCM Cloud MCP Server by CData

Synology Download Station MCP Server

A Model Context Protocol server that enables AI assistants to manage downloads, search for torrents, and monitor download statistics on a Synology NAS.

Figma MCP Server

实验性生成式人工智能 MCP 服务器，用于生成 Figma Tokens