Discover Awesome MCP Servers

IMAP MCP Server

Enables comprehensive interaction with IMAP email accounts through 38 tools for reading, searching, and organizing messages. It supports advanced features like real-time mailbox monitoring via IDLE watch and automated email archiving.

AgentBroker MCP Server

AI-native cryptocurrency exchange built for autonomous agents. Register, deposit USDC, select a strategy, and trade 8 crypto pairs (BTC, ETH, SOL + more) programmatically — no KYC required. Includes sandbox with 10,000 virtual USDC for testing.

MCP Nautobot Server

A Model Context Protocol server that integrates with Nautobot to provide network automation and infrastructure data to AI assistants like Claude, allowing them to query and interact with network Source of Truth systems.

Promokit Prestashop MCP Server

A Model Context Protocol server designed to integrate with Claude Desktop, allowing users to interact with Prestashop e-commerce platforms through natural language interfaces.

apisetu-mcp-server

Servidor MCP de API Setu

Octocode MCP

AI-powered code assistant that provides advanced search and discovery capabilities across GitHub and NPM ecosystems, helping users understand code patterns, implementations, and connections between repositories.

Kali Pentest MCP Server

Provides secure access to penetration testing tools from Kali Linux including nmap, nikto, dirb, wpscan, and sqlmap for educational vulnerability assessment. Operates in a controlled Docker environment with target whitelisting to ensure ethical testing practices.

zotero-assistant-mcp

A Zotero library management MCP server designed for Cloudflare Workers that enables searching, reading, and writing library items. It allows users to manage metadata, full-text content, and attachments through natural language interactions.

SmartconversionAPI

conversion image to webp/avif with 402 payement required, 0.001 and 0.0005 after 1000 requete.

Remote MCP Server (Authless)

A template for deploying an authentication-free MCP server on Cloudflare Workers. Allows users to create and customize remote MCP tools accessible from Claude Desktop or AI playgrounds via SSE endpoint.

Jira MCP Server

A simple MCP server that provides access to Jira issues from Cursor AI, allowing users to reference and query Jira tickets directly in the chat panel.

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 es un servidor del Protocolo de Contexto de Modelo (MCP) que permite a asistentes de IA como Claude ejecutar comandos de Kubernetes de forma segura. Proporciona un puente entre los modelos de lenguaje y las herramientas esenciales de la CLI de Kubernetes, incluyendo kubectl, helm, istioctl y argocd, permitiendo que los sistemas de IA ayuden con la gestión de clústeres, la resolución de problemas y las implementaciones.

qmcp

An MCP server that enables AI assistants to interact with q/kdb+ databases for development and debugging workflows. It supports executing queries, persistent connection management, and includes a Qython translator for converting Python-like syntax to q.

Sloot MCP Server

A TypeScript MCP server implementation using Express.js that provides basic tools like echo, time retrieval, and calculator functionality. Features session management, RESTful API endpoints, and Server-Sent Events for streamable communication.

XMCP

A comprehensive MCP server for X/Twitter featuring over 70 tools for research, engagement, and publishing with granular permission-based access control. It includes specialized Playwright-powered tools for fetching X articles and supports extensive account management and thread operations.

Semantic Scholar MCP Server

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

Cortex Context MCP Adapter

Enables integration with Cortex Context services through the Model Context Protocol. Provides authenticated access to CortexGuardAI's context management capabilities for registered users.

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.

MCP demo (DeepSeek as Client's LLM)

Okay, I understand. You want me to provide instructions on how to run a Minimal Communication Protocol (MCP) client and server demo, using the DeepSeek API for some aspect of the communication. Since I don't know the specifics of your MCP implementation or how you intend to use the DeepSeek API, I'll provide a general outline and examples. You'll need to adapt this to your specific code and environment. **Assumptions:** * You have a basic understanding of Python and networking concepts (sockets). * You have a DeepSeek API key and know how to use it. * You have a working MCP client and server implementation (even a very basic one). * You want to use the DeepSeek API for something like: * Generating responses on the server side. * Analyzing messages sent between the client and server. * Generating test data for the client and server. **General Outline:** 1. **Set up your environment:** * Install necessary libraries (e.g., `socket`, `requests` for DeepSeek API). * Set your DeepSeek API key as an environment variable or store it securely. 2. **Basic MCP Client and Server (Without DeepSeek):** * Create a simple server that listens for connections and receives messages. * Create a simple client that connects to the server and sends messages. * Test that the basic communication works. 3. **Integrate DeepSeek API:** * Decide *where* and *how* you want to use the DeepSeek API. * Add the necessary code to call the DeepSeek API. * Handle the API response and integrate it into your MCP communication. 4. **Run the Demo:** * Start the server. * Start the client. * Observe the communication and the DeepSeek API interaction. **Example Code (Python):** **1. Environment Setup:** ```bash # Install necessary libraries pip install requests ``` **2. Basic MCP Server (server.py):** ```python import socket HOST = '127.0.0.1' # Standard loopback interface address (localhost) PORT = 65432 # Port to listen on (non-privileged ports are > 1023) with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind((HOST, PORT)) s.listen() print(f"Server listening on {HOST}:{PORT}") conn, addr = s.accept() with conn: print(f"Connected by {addr}") while True: data = conn.recv(1024) if not data: break message = data.decode('utf-8') print(f"Received: {message}") conn.sendall(data) # Echo back the message ``` **3. Basic MCP Client (client.py):** ```python import socket HOST = '127.0.0.1' # The server's hostname or IP address PORT = 65432 # The port used by the server with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.connect((HOST, PORT)) message = "Hello, server!" s.sendall(message.encode('utf-8')) data = s.recv(1024) print(f"Received: {data.decode('utf-8')}") ``` **4. Integrating DeepSeek API (Example: Server-Side Response Generation):** ```python import socket import requests import os # For accessing environment variables HOST = '127.0.0.1' PORT = 65432 DEEPSEEK_API_KEY = os.environ.get("DEEPSEEK_API_KEY") # Get API key from environment def generate_deepseek_response(prompt): """Calls the DeepSeek API to generate a response.""" if not DEEPSEEK_API_KEY: return "Error: DeepSeek API key not found. Please set the DEEPSEEK_API_KEY environment variable." url = "YOUR_DEEPSEEK_API_ENDPOINT" # Replace with the actual DeepSeek API endpoint headers = { "Authorization": f"Bearer {DEEPSEEK_API_KEY}", "Content-Type": "application/json" } data = { "prompt": prompt, "max_tokens": 50 # Adjust as needed # Add other parameters as required by the DeepSeek API } try: response = requests.post(url, headers=headers, json=data) response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx) json_response = response.json() return json_response["choices"][0]["text"].strip() # Adjust based on API response format except requests.exceptions.RequestException as e: return f"Error calling DeepSeek API: {e}" except (KeyError, IndexError) as e: return f"Error parsing DeepSeek API response: {e}" with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind((HOST, PORT)) s.listen() print(f"Server listening on {HOST}:{PORT}") conn, addr = s.accept() with conn: print(f"Connected by {addr}") while True: data = conn.recv(1024) if not data: break message = data.decode('utf-8') print(f"Received: {message}") # Use DeepSeek API to generate a response based on the received message deepseek_prompt = f"Respond to the following message: {message}" deepseek_response = generate_deepseek_response(deepseek_prompt) print(f"DeepSeek Response: {deepseek_response}") conn.sendall(deepseek_response.encode('utf-8')) # Send the DeepSeek response back ``` **Important Considerations and Adaptations:** * **Replace Placeholders:** Fill in the `YOUR_DEEPSEEK_API_ENDPOINT` with the actual DeepSeek API endpoint. Adjust the `data` dictionary in `generate_deepseek_response` to match the API's required parameters. Also, adjust the parsing of the `json_response` to match the API's response format. * **Error Handling:** The example includes basic error handling for the DeepSeek API call. Add more robust error handling as needed. * **API Rate Limits:** Be mindful of the DeepSeek API's rate limits. Implement appropriate delays or retry mechanisms to avoid exceeding the limits. * **Security:** Never hardcode your API key directly into your code. Use environment variables or a secure configuration file. * **DeepSeek API Usage:** The example uses the DeepSeek API to generate a response to the client's message. You can adapt this to other use cases, such as: * **Analyzing the message:** Send the message to the DeepSeek API for sentiment analysis or topic extraction. * **Generating test data:** Use the DeepSeek API to create realistic test messages for your client and server. * **Validating messages:** Use the DeepSeek API to check if a message conforms to a specific format or contains certain keywords. * **MCP Protocol:** This example uses a very basic "echo" protocol. Adapt the code to your specific MCP protocol. You might need to handle different message types, headers, and data formats. * **Asynchronous Operations:** For more complex applications, consider using asynchronous operations (e.g., `asyncio`) to handle multiple client connections and API calls concurrently. **How to Run the Demo:** 1. **Save the code:** Save the server code as `server.py` and the client code as `client.py`. 2. **Set the API key:** Set the `DEEPSEEK_API_KEY` environment variable: ```bash export DEEPSEEK_API_KEY="YOUR_DEEPSEEK_API_KEY" # Replace with your actual API key ``` 3. **Run the server:** ```bash python server.py ``` 4. **Run the client:** ```bash python client.py ``` You should see the client connect to the server, send a message, and receive a response generated by the DeepSeek API. The server will print the received message and the DeepSeek API response. Remember to adapt this example to your specific MCP implementation and DeepSeek API use case. Good luck!

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.

orchex

Autopilot AI orchestration — auto-plan, parallelize, self-heal, and route across 6 LLMs. Describe what you want. Orchex plans, parallelizes, and executes — safely.

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.

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.

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.

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.