Discover Awesome MCP Servers

Dingo MCP Server

Dingo MCP Server

Aegis

A compliance kernel for MCP that enforces policy rules between AI agents and data systems, providing deterministic access control, PII masking, and audit trails.

cisco-sdwan-mcp

An MCP server for Cisco Catalyst SD-WAN that lets LLM clients query fabric inventory, device health, alarms, and path quality from vManage. It is read-only by default, with configuration-changing tools available only when explicitly enabled and requiring human confirmation.

Kaiten MCP Server

MCP server for interacting with Kaiten (kaiten.ru) project management. Supports listing, moving tasks, setting responsible persons, and adding comments via natural language.

mcp-1panel

Full-featured MCP server for 1Panel server management panel. Provides 490+ tools for managing websites, databases, containers, files, and more through natural language.

mcp-leave-management

Exposes employee leave-management operations (add employees, check balance, apply/cancel leave, view history) as MCP tools for AI clients like Claude Desktop to call directly.

EasyHunt-AI

MCP server for AI-driven VAPT orchestration, enabling agents to plan and execute authorized security scans through a control plane that enforces scope, sanitization, budget, rate limits, human approval, and audit logging.

vCluster YAML MCP Server

Enables AI assistants to query, validate, and create vCluster YAML configurations directly from GitHub, supporting version-specific queries and automatic validation.

google-flow-mcp-server

Automates sports editorial graphic poster creation using Google Flow through a Playwright-based pipeline, exposing a FastMCP server for integration with MCP clients.

vet-mcp

vet-mcp

Seed MCP Server

Enables creating and inspecting Seed tables and relationships through the Seed backend HTTP API.

sticky-notes-server

A MCP server for managing sticky notes with CRUD operations, search, tags, conversations, and a React UI.

Strava MCP Server

Un servidor de TypeScript que actúa como puente a la API de Strava, permitiendo a los LLM (Modelos de Lenguaje Grandes) acceder a las actividades, rutas, segmentos y datos de atletas de los usuarios a través de la interacción en lenguaje natural.

DevSpace Ultra

An MIT-licensed DevSpace distribution with an elastic ChatGPT Classic multi-agent runtime layer for managing multiple independent worker conversations and scaling them dynamically.

Rohlik MCP

Enables AI assistants to interact with Rohlik Group's online grocery delivery services across multiple countries, including product search, cart management, and account info.

CryptoToolBox MCP Server

A free, zero-dependency Model Context Protocol (MCP) server for AI agents providing Web3/crypto toolkit (balances, ENS, token info, tx decode, Chainlink price feed, payment verification) plus encoding, hashing, and data transformation utilities.

econdata

Wraps the Bureau of Labor Statistics public API v2 to provide economic data through natural language queries or direct tool calls.

tiktok-trends-mcp

Provides TikTok hashtag trend data including volume growth, viral spikes, and historical time series, enabling AI assistants to spot emerging trends before they go mainstream.

Wikipedia Summarizer MCP Server

Un servidor MCP (Protocolo de Contexto del Modelo) que busca y resume artículos de Wikipedia utilizando LLMs de Ollama, accesible tanto a través de la línea de comandos como de interfaces Streamlit. Perfecto para extraer rápidamente información clave de Wikipedia sin tener que leer artículos completos.

MCP Troubleshooter

A specialized diagnostic framework that enables AI models to self-diagnose and fix MCP-related issues by analyzing logs, validating configurations, testing connections, and implementing solutions.

skype-archive-mcp

Enables local browsing and searching of Skype export archives through MCP tools for messages, conversations, participants, and attachment extraction, with privacy-focused local processing.

Civic Data MCP Server

Provides access to 7 free government and open data APIs including NOAA weather, US Census demographics, NASA imagery, World Bank economics, Data.gov, and EU Open Data through 22 specialized tools, with most requiring no API keys.

MCP DeepSeek 演示项目

Okay, here's a minimal example of using DeepSeek (assuming you mean DeepSeek LLM or a similar model) combined with MCP (MicroConfig Protocol) in a client-server scenario. This is a simplified illustration and would need adaptation based on your specific DeepSeek model and MCP implementation. **Conceptual Overview:** * **MCP (MicroConfig Protocol):** A lightweight protocol for configuration management. In this example, we'll use it to send a prompt to the DeepSeek server and receive the generated text. We'll assume a simple key-value pair structure for MCP messages. * **DeepSeek Server:** A server that hosts the DeepSeek LLM. It receives prompts via MCP, generates text, and sends the response back via MCP. * **Client:** A client that sends a prompt to the DeepSeek server via MCP and displays the response. **Simplified Code Examples (Python):** **1. DeepSeek Server (server.py):** ```python import socket import json # Assuming you have a way to interact with your DeepSeek model # (e.g., using a DeepSeek API or a local model) # Replace this with your actual DeepSeek interaction code. def generate_text_with_deepseek(prompt): """ Placeholder for DeepSeek model interaction. Replace with your actual DeepSeek API call or model inference. """ # Simulate DeepSeek response if "translate" in prompt.lower(): response = "Hola mundo!" # Example Spanish translation else: response = f"DeepSeek says: {prompt}" return response def handle_client(conn, addr): print(f"Connected by {addr}") try: while True: data = conn.recv(1024) # Receive up to 1024 bytes if not data: break try: # MCP: Assume JSON-based key-value pairs message = json.loads(data.decode('utf-8')) prompt = message.get("prompt") if prompt: generated_text = generate_text_with_deepseek(prompt) response_message = {"response": generated_text} conn.sendall(json.dumps(response_message).encode('utf-8')) else: error_message = {"error": "No prompt provided"} conn.sendall(json.dumps(error_message).encode('utf-8')) except json.JSONDecodeError: error_message = {"error": "Invalid JSON format"} conn.sendall(json.dumps(error_message).encode('utf-8')) except Exception as e: error_message = {"error": f"Server error: {str(e)}"} conn.sendall(json.dumps(error_message).encode('utf-8')) except ConnectionResetError: print(f"Connection reset by {addr}") finally: conn.close() print(f"Connection closed with {addr}") def start_server(): 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"Listening on {HOST}:{PORT}") while True: conn, addr = s.accept() handle_client(conn, addr) if __name__ == "__main__": start_server() ``` **2. Client (client.py):** ```python import socket import json HOST = "127.0.0.1" # The server's hostname or IP address PORT = 65432 # The port used by the server def send_prompt(prompt): with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: try: s.connect((HOST, PORT)) message = {"prompt": prompt} s.sendall(json.dumps(message).encode('utf-8')) data = s.recv(1024) response = json.loads(data.decode('utf-8')) print(f"Received: {response}") except ConnectionRefusedError: print("Connection refused. Is the server running?") except Exception as e: print(f"Error: {e}") if __name__ == "__main__": user_prompt = input("Enter a prompt for DeepSeek: ") send_prompt(user_prompt) ``` **How to Run:** 1. **Install Dependencies:** You'll need Python installed. No external libraries are strictly required for this minimal example, but you'll need to install the DeepSeek API client library if you're using a remote DeepSeek service. 2. **Replace Placeholder:** In `server.py`, **replace the `generate_text_with_deepseek` function with your actual DeepSeek model interaction code.** This is the crucial part where you integrate with the DeepSeek LLM. This will likely involve using an API key, setting model parameters, and handling the API response. 3. **Start the Server:** Run `python server.py` in a terminal. 4. **Run the Client:** Run `python client.py` in another terminal. The client will prompt you to enter text. Type a prompt and press Enter. The client will send the prompt to the server, the server will process it with DeepSeek, and the client will display the response. **Explanation:** * **MCP (Simplified):** The code uses JSON to encode and decode messages between the client and server. This is a simple form of MCP. A real MCP implementation might have more sophisticated features like versioning, error handling, and data validation. * **Sockets:** The code uses Python's `socket` module for network communication. The server listens for incoming connections, and the client connects to the server. * **Error Handling:** The code includes basic error handling for connection errors, JSON parsing errors, and server-side exceptions. * **DeepSeek Integration (Placeholder):** The `generate_text_with_deepseek` function is a placeholder. You **must** replace it with your actual DeepSeek API call or model inference code. This is the core of the integration. **Important Considerations:** * **DeepSeek API/Model:** This example assumes you have access to a DeepSeek LLM, either through an API or a local model. You'll need to obtain API credentials or set up your local model environment. * **Security:** This is a very basic example and does not include any security measures. In a production environment, you would need to implement authentication, authorization, and encryption. * **Scalability:** This example is not designed for high scalability. For production use, you would need to consider using a more robust server architecture and load balancing. * **MCP Implementation:** This example uses a very simple JSON-based MCP. A real MCP implementation might use a more efficient binary format or a more sophisticated protocol. * **Error Handling:** The error handling is basic. You should add more robust error handling and logging for production use. **Example Interaction:** 1. **Run `server.py`** 2. **Run `client.py`** 3. **Client Prompt:** `Translate "Hello world!" to Spanish` 4. **Client Output:** `Received: {'response': 'Hola mundo!'}` This minimal example provides a starting point for integrating DeepSeek with MCP. You'll need to adapt it to your specific requirements and environment. Remember to replace the placeholder code with your actual DeepSeek interaction logic. Good luck!

Trader Journal MCP

Provides AI-powered trading analytics and coaching by analyzing historical trades to identify strategies, emotional patterns, and behavioral mistakes via MCP tools.

DevLog MCP

Enables AI coding agents to publish development logs to a Notion blog database via a single tool, handling input validation and Notion storage.

Terminal MCP Server

Exposes authenticated shell tools for remote command execution and task management, enabling grok.com to run shell commands on the host machine.

jobjourney-claude-plugin

An MCP server that enables AI-assisted job search workflows including job discovery, application tracking, resume evaluation, and cover letter generation, with support for multiple job sources and scheduled scraping.

usgswater

Wraps USGS NWIS REST services to query water data such as streamflow, groundwater levels, and water quality.

DateTime MCP Server

Provides timezone-aware date and time information with configurable time formats and timezone support. Enables users to get current date and time in their preferred timezone and format through simple MCP tools.

trm-mcp

A local, offline-first MCP server for searching, grepping, reading, rendering, and comparing long datasheet/TRM PDFs, using hybrid retrieval with BM25, dense embeddings, and visual page indexing.