Discover Awesome MCP Servers

MCP Server Tester

A web app to test MCP servers using an installation code from Smithery.

Open Brain MCP Server

A personal semantic knowledge base that enables storing, searching, and retrieving memories and work history using natural language. It features vector-based search, tool discovery via a registry, and indexing of Cursor agent transcripts using Supabase or Postgres.

Multi-Capability Proxy Server

A Flask-based server that hosts multiple tools, each exposing functionalities by calling external REST APIs through a unified interface.

Weather MCP Server

Enables AI assistants to retrieve real-time weather data and 5-day forecasts for any city using the OpenWeather API, supporting both metric and imperial units.

SEQ MCP Server

Enables LLMs to query and analyze logs from SEQ structured logging server with capabilities for searching events, retrieving event details, analyzing log patterns, and accessing saved searches.

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.

Oracle HCM Cloud MCP Server by CData

Oracle HCM Cloud MCP Server by CData

Vertica MCP Server

Enables AI assistants to query and explore Vertica databases through natural language with readonly protection by default. Supports SQL execution, schema discovery, large dataset streaming, and Vertica-specific optimizations like projection awareness.

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.

ServiceDesk Plus MCP Server

A Model Context Protocol server for integrating with ServiceDesk Plus On-Premise that provides comprehensive CMDB functionality, allowing users to manage tickets, assets, software licenses, contracts, vendors, and administrative settings through natural language.

Swagger to MCP

Automatically converts Swagger/OpenAPI specifications into dynamic MCP tools, enabling interaction with any REST API through natural language by loading specs from local files or URLs.

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.

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。

OSRS-STAT

A Model Context Protocol (MCP) server that provides real-time player statistics and ranking data of 'Old School RuneScape', supporting multiple game modes and player comparison functions.

Agent Interviews

Agent Interviews

Semantic Scholar MCP Server

Semantic Scholar API, providing comprehensive access to academic paper data, author information, and citation networks.

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!

Apple Doc MCP

A Model Context Protocol server that provides AI coding assistants with direct access to Apple's Developer Documentation, enabling seamless lookup of frameworks, symbols, and detailed API references.

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.

claude-peers

Enables discovery and instant communication between multiple local Claude Code instances running across different projects. It allows agents to list active peers, share work summaries, and send messages through a local broker daemon.

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.

mcp-shell

Give hands to AI. MCP server to run shell commands securely, auditably, and on demand.

Layout Detector MCP

Analyzes webpage screenshots to extract precise layout information by locating image assets and calculating spatial relationships, enabling AI assistants to accurately recreate layouts with proper semantic structure using computer vision.

JMeter MCP Server

Enables the execution and analysis of JMeter performance tests through MCP-compatible clients. It provides tools for running tests in non-GUI mode, identifying performance bottlenecks, and generating comprehensive insights and visualizations from result files.

BRAINS OS - version MCP

一个使用 SST、React 和 AWS 构建的 Serverless MCP 实现。

MCPizza

An MCP server that allows AI assistants to order Domino's Pizza through an unofficial API, with features for store location, menu browsing, and order management.

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.

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.