Discover Awesome MCP Servers

FortunaMCP

FortunaMCP is an advanced MCP server dedicated to generating high-quality random values. It leverages the Fortuna C-extension, which is directly powered by Storm—a robust, thread-safe C++ RNG engine optimized for high-speed, hardware-based entropy.

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-diagram

This is a bit ambiguous. "MCP server" doesn't have a standard meaning in the context of diagramming. To give you the best translation, I need to understand what you mean by "MCP server." Here are a few possibilities and their translations: **1. If you mean a server that *hosts* a diagramming application or service (like a web server running a diagramming tool):** * **Spanish:** *Servidor para diagramación* (This is the most general and likely correct translation) * **Spanish (more specific):** *Servidor de aplicaciones para diagramación* (Application server for diagramming) * **Spanish (if it's a web server):** *Servidor web para diagramación* (Web server for diagramming) **2. If "MCP" is an acronym for a specific diagramming software or protocol (you'll need to tell me what it stands for):** * **Example (if MCP stood for "My Cool Program"):** *Servidor de My Cool Program para diagramación* (My Cool Program server for diagramming) - You would replace "My Cool Program" with the actual name. **3. If you're referring to a server that *processes* diagrams (e.g., a server that takes diagram data as input and generates an image or performs some analysis):** * **Spanish:** *Servidor de procesamiento de diagramas* (Diagram processing server) **4. If you're referring to a server that *stores* diagrams:** * **Spanish:** *Servidor de almacenamiento de diagramas* (Diagram storage server) **To help me give you the best translation, please tell me:** * **What does "MCP" stand for (if anything)?** * **What is the server *doing* in relation to the diagramming?** Is it hosting the application, processing the diagrams, storing them, or something else? Once I have this information, I can provide a much more accurate and helpful translation.

Redis MCP

Enables AI assistants to perform comprehensive Redis database operations including managing strings, hashes, lists, sets, sorted sets, TTL management, and data backup/restore. Supports secure connections and provides batch operations for efficient Redis interaction through natural language.

AWS Security MCP

A Model Context Protocol server that connects AI assistants like Claude to AWS security services, allowing them to autonomously query, inspect, and analyze AWS infrastructure for security issues and misconfigurations.

Europe PMC Literature Search MCP Server

A professional literature search tool built on FastMCP framework that enables AI assistants to search academic literature from Europe PMC, retrieve article details, and analyze journal quality with seamless integration into Claude Desktop and Cherry Studio.

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.

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!

Haloscan MCP Server

A Model Context Protocol server that exposes Haloscan SEO API functionality, allowing users to access keyword insights, domain analysis, and competitor research through Claude for Desktop and other MCP-compatible clients.

interzoid-get-weather-by-zip-code-api

An MCP Server that provides weather information by ZIP code using Interzoid's API, allowing agents to retrieve current weather conditions based on postal codes.

ClamAV MCP

A server that enables scanning files for viruses using the ClamAV engine, providing a simple integration with Cursor IDE via SSE connections.

Consult LLM MCP

An MCP server that lets Claude Code consult stronger AI models (o3, Gemini 2.5 Pro, DeepSeek Reasoner) when you need deeper analysis on complex problems.

mcp-server-logs-sieve

An MCP server that connects Claude (or any MCP compatible client) to your existing log infrastructure. Query, summarize, and trace logs in plain English across GCP Cloud Logging, AWS CloudWatch, Azure Log Analytics, Grafana Loki, and Elasticsearch without writing filter expressions or leaving your editor.

AgentWallet MCP Server

Provides permissionless wallet infrastructure for AI agents to manage wallets, sign transactions, and handle tokens across Solana and all EVM-compatible chains. It includes 29 specialized tools for on-chain operations, featuring built-in security guards and automated x402 payment processing without KYC requirements.

BloodHound MCP

Una extensión que permite a los Modelos de Lenguaje Extensos interactuar y analizar entornos de Active Directory a través de consultas en lenguaje natural en lugar de consultas Cypher manuales.

MCP Weather Sample

A demonstration project for Model Context Protocol (MCP) that integrates weather services with multiple AI models (Claude, GPT, Gemini), enabling natural language queries for weather alerts and forecasts.

srclight

Deep code indexing for AI agents. Search symbols, navigate call graphs, explore inheritance, track git history — all via MCP.

MCP Poisoning Attack - PoC

Este repositorio demuestra una variedad de **Ataques de Envenenamiento de MCP** que afectan los flujos de trabajo de agentes de IA del mundo real.

xcomet-mcp-server

Translation quality evaluation MCP Server powered by xCOMET. Provides quality scoring (0-1), error detection with severity levels, and optimized batch processing for AI-assisted translation workflows.

PDF Export for AI Agents

Well-designed PDFs from a single prompt. Describe what you need, get a professional document.

Amazon MCP Server

Enables users to search for products, retrieve details, and manage their shopping cart on Amazon through the MCP framework. It also supports viewing order history and provides a demonstration of ordering capabilities within AI interfaces.

MSTP-MCP

MSTP writing style MCP server for all.

Withings MCP Client

Enables retrieval of health data from Withings smart scales including weight measurements and comprehensive body composition metrics like fat mass, muscle mass, and hydration levels. Supports multiple users, unit preferences, and OAuth authentication for secure access to personal health data.

LibSQL Memory

Un servidor MCP de alto rendimiento que utiliza libSQL para memoria persistente y capacidades de búsqueda vectorial, lo que permite una gestión eficiente de entidades y el almacenamiento de conocimiento semántico.

MCP Gateway

A production-ready unified entry point for AI agents that implements the Model Context Protocol (MCP). It provides a secure gateway with rate limiting, authentication, and observability for managing and proxying requests to multiple downstream APIs.

Apple Calendar MCP Server

Provides Claude with full access to Apple Calendar on macOS for event management, smart scheduling, and schedule analytics. It enables natural language event creation, conflict detection, and template-based scheduling through AppleScript integration.

Searchclaw

Description: An MCP server with 15 tools covering web search, scraping, extraction, crawling, and autonomous data gathering via the SearchClaw API. Tagline: "The complete web data pipeline for AI agents — Search, Extract, Crawl in One API."

pdf-mcp

An MCP server that provides tools for reading, writing, and manipulating PDF files, including text extraction, metadata retrieval, and merging or splitting documents. It also enables users to create PDFs from plain text and convert specific pages or entire documents into images.

AskTable MCP Server

Enables interaction with AskTable's data analytics platform through MCP protocol. Supports querying and analyzing data from connected datasources using natural language, available via both SaaS and self-hosted deployments.