Discover Awesome MCP Servers

PuchAI MCP Server

A multi-purpose MCP server that enables Reddit research for market validation, data visualization with charts, medicine information lookup, user preference management, and task management tools. Provides comprehensive utilities for research, data analysis, and personal productivity through natural language interactions.

dy-mcp

Enables automation of Douyin (TikTok China) tasks including parsing share links to get watermark-free download URLs and uploading videos from specified local paths.

Multi-Feature MCP Server

Provides comprehensive functionality including weather data, system utilities, Azure cloud management, and AI-powered image generation and editing. Features interactive parameter selection through MCP elicitation for enhanced user experience.

Python MCP Server Examples

A collection of Python-based Model Context Protocol servers that extend AI assistant capabilities with tools for calculations, AWS services (S3 and RDS), and PostgreSQL database operations.

Property MCP Server

Connects Claude AI to real estate data via the ATTOM Data API, enabling property information lookup and analysis using natural language queries.

MCP AI Memory

Enables AI agents to store, retrieve, and manage contextual knowledge across sessions using semantic search with PostgreSQL and vector embeddings. Supports memory relationships, clustering, multi-agent isolation, and intelligent caching for persistent conversational context.

AverbePorto-MCP

Servidor MCP AverbePorto

Build MCP Server

Enables AI assistants to manage development workflows by running build commands, executing tests, analyzing package.json files, installing dependencies, and performing code linting. Supports multiple package managers (npm, yarn, pnpm) and provides detailed error reporting for development operations.

Alethea World History Engine

A narrative graph engine that enables LLMs to generate, track, and mutate complex fictional worlds while maintaining consistency between factions, characters, and locations. It acts as a specialized RAG framework for storytelling, allowing models to manage thousands of entities without exceeding context limits.

Custom MCP Database Server

A Middleware/Control Plane server that allows AI code agents to securely execute queries against various databases (PostgreSQL, MySQL, MongoDB, Oracle) without directly exposing credentials.

Interactive Brokers MCP Server

Connects AI assistants to Interactive Brokers for intelligent portfolio management, options analysis, risk monitoring, and automated trading strategy suggestions. Enables real-time account tracking, Greeks calculations, option chain analysis, and playbook-based risk adjustments through natural language.

Android MCP

A lightweight bridge enabling AI agents to perform real-world tasks on Android devices such as app navigation, UI interaction, and automated QA testing without requiring computer-vision pipelines or preprogrammed scripts.

MCP-demo-blog-analyzer

Aquí tienes un inicio rápido para probar el cliente del analizador de blogs MCP y un servidor de visitantes de páginas web: **Título:** Inicio Rápido: Probando el Cliente del Analizador de Blogs MCP y el Servidor de Visitantes de Páginas Web **Introducción:** Esta guía te proporcionará los pasos básicos para configurar y probar rápidamente el cliente del analizador de blogs MCP y un servidor de visitantes de páginas web. Asumimos que tienes un conocimiento básico de desarrollo de software y herramientas de línea de comandos. **Pasos:** 1. **Configuración del Entorno:** * Asegúrate de tener instalado un entorno de desarrollo adecuado (por ejemplo, Python, Node.js, Java). * Instala las dependencias necesarias para el cliente del analizador de blogs MCP y el servidor de visitantes de páginas web. Esto generalmente se hace usando un gestor de paquetes como `pip` (para Python), `npm` (para Node.js) o Maven/Gradle (para Java). Consulta la documentación específica de cada proyecto para conocer las dependencias exactas. * Ejemplo (Python): `pip install requests beautifulsoup4` 2. **Descarga/Clonación del Código:** * Obtén el código fuente del cliente del analizador de blogs MCP y del servidor de visitantes de páginas web. Esto podría implicar descargar un archivo ZIP o clonar un repositorio Git. * Ejemplo (Git): `git clone <URL_DEL_REPOSITORIO_DEL_CLIENTE>` y `git clone <URL_DEL_REPOSITORIO_DEL_SERVIDOR>` 3. **Configuración del Cliente del Analizador de Blogs MCP:** * Configura el cliente del analizador de blogs MCP para que apunte a una URL de blog de prueba. Esto generalmente implica modificar un archivo de configuración o pasar la URL como un argumento de línea de comandos. * Asegúrate de que el cliente tenga las credenciales necesarias (si las hay) para acceder al blog. 4. **Configuración del Servidor de Visitantes de Páginas Web:** * Configura el servidor de visitantes de páginas web para que escuche en un puerto específico (por ejemplo, 8080). * Define las rutas (endpoints) que el servidor expondrá para recibir datos del cliente del analizador de blogs MCP. * Configura una base de datos (si es necesario) para almacenar los datos de los visitantes. 5. **Ejecución del Servidor de Visitantes de Páginas Web:** * Inicia el servidor de visitantes de páginas web. Esto generalmente se hace ejecutando un script o un comando desde la línea de comandos. * Ejemplo (Python): `python server.py` 6. **Ejecución del Cliente del Analizador de Blogs MCP:** * Ejecuta el cliente del analizador de blogs MCP. Esto generalmente se hace ejecutando un script o un comando desde la línea de comandos. * Ejemplo (Python): `python analyzer.py <URL_DEL_BLOG>` 7. **Verificación de Resultados:** * Verifica que el cliente del analizador de blogs MCP se ejecute correctamente y envíe datos al servidor de visitantes de páginas web. * Verifica que el servidor de visitantes de páginas web reciba los datos y los almacene correctamente (si corresponde). * Utiliza un navegador web para acceder a las rutas (endpoints) del servidor y verificar que los datos de los visitantes se muestren correctamente. **Ejemplo Simplificado (Python):** **`analyzer.py` (Cliente del Analizador de Blogs MCP):** ```python import requests from bs4 import BeautifulSoup def analyze_blog(url): try: response = requests.get(url) response.raise_for_status() # Lanza una excepción para códigos de error HTTP soup = BeautifulSoup(response.content, 'html.parser') # Aquí iría la lógica para analizar el blog (ejemplo: contar párrafos) paragraph_count = len(soup.find_all('p')) print(f"Número de párrafos en {url}: {paragraph_count}") # Enviar los datos al servidor (reemplaza con la URL correcta) server_url = "http://localhost:8080/visit" data = {"url": url, "paragraphs": paragraph_count} requests.post(server_url, json=data) print(f"Datos enviados a {server_url}") except requests.exceptions.RequestException as e: print(f"Error al acceder a la URL: {e}") if __name__ == "__main__": blog_url = "https://www.ejemplo.com" # Reemplaza con una URL real analyze_blog(blog_url) ``` **`server.py` (Servidor de Visitantes de Páginas Web - usando Flask):** ```python from flask import Flask, request, jsonify app = Flask(__name__) @app.route('/visit', methods=['POST']) def visit(): data = request.get_json() print(f"Datos recibidos: {data}") # Aquí iría la lógica para almacenar los datos (ejemplo: en una base de datos) return jsonify({"status": "success", "message": "Datos recibidos correctamente"}), 200 if __name__ == '__main__': app.run(debug=True, port=8080) ``` **Instrucciones para el ejemplo:** 1. **Instala Flask:** `pip install flask` 2. **Guarda los códigos:** Guarda el primer código como `analyzer.py` y el segundo como `server.py`. 3. **Ejecuta el servidor:** Abre una terminal y ejecuta `python server.py`. 4. **Ejecuta el cliente:** Abre otra terminal y ejecuta `python analyzer.py`. Reemplaza `"https://www.ejemplo.com"` con una URL real. 5. **Verifica:** Observa la salida en ambas terminales. El servidor debería mostrar los datos recibidos del cliente. **Consideraciones Adicionales:** * **Manejo de Errores:** Implementa un manejo de errores robusto en ambos componentes. * **Seguridad:** Considera las implicaciones de seguridad, especialmente si el servidor está expuesto a la red. * **Escalabilidad:** Si planeas manejar un gran volumen de datos, considera utilizar una base de datos más robusta y técnicas de escalabilidad. * **Documentación:** Consulta la documentación específica del cliente del analizador de blogs MCP y del servidor de visitantes de páginas web para obtener información más detallada. Este inicio rápido te proporciona una base para comenzar a probar tus componentes. A partir de aquí, puedes expandir y refinar la funcionalidad según tus necesidades específicas. Recuerda reemplazar las URLs de ejemplo con las URLs reales de tu blog y servidor.

MIDI Analyzer MCP Server

Enables comprehensive analysis of MIDI SMF files including loading, parsing, track analysis, event extraction with filtering, and detailed file summaries. Supports memory management for efficient repeated access to loaded MIDI files.

Usage And Billing MCP Server

Enables access to Usage and Billing APIs for managing accounts, products, meters, plans, and usage reporting. Supports operations like creating products/plans, reporting usage, and retrieving billing information.

Microsoft Learn Docs MCP Server

The Microsoft Learn Docs MCP Server is a cloud-hosted service that enables clients like GitHub Copilot and other AI agents to bring trusted and up-to-date information directly from Microsoft's official documentation. It is a remote MCP Server using streamable http, which is lightweight for clients t

Sequential Thinking MVP Server

Enables AI assistants to perform structured, step-by-step reasoning by breaking down complex problems into numbered thoughts, with support for revising previous steps and exploring alternative reasoning paths.

Appointment Manager MCP Server

Enables users to manage appointments through a FastAPI backend with PostgreSQL database. Supports creating, listing, updating, and deleting appointments via natural language interactions through Claude.

MCP Serverless Functions Example

A basic example of developing and running serverless Model Context Protocol (MCP) using Netlify Functions, demonstrating how to deploy and access serverless functions with customized URLs.

FM8 MCP Server

Enables AI assistants to control Native Instruments FM8 synthesizer parameters through MIDI CC messages, allowing natural language programming of FM synthesis sounds including operator routing, frequency ratios, and modulation matrix settings.

Math Agent with Microsoft Word and Gmail Integration

Here are a few ways to translate "MCP server for Math Agent with Microsoft Word and Gmail Integration" into Spanish, depending on the nuance you want to convey: **Option 1 (Most Literal and General):** * **Servidor MCP para Agente Matemático con Integración de Microsoft Word y Gmail.** * This is the most direct translation and is suitable for most technical contexts. **Option 2 (Slightly More Natural Flow):** * **Servidor MCP para un Agente Matemático con Integración en Microsoft Word y Gmail.** * Adding "un" (a/an) before "Agente Matemático" can make it sound slightly more natural in Spanish. **Option 3 (Emphasizing Functionality):** * **Servidor MCP para un Agente Matemático integrado con Microsoft Word y Gmail.** * Using "integrado con" (integrated with) emphasizes the functionality of the integration. **Explanation of Terms:** * **MCP Server:** "Servidor MCP" remains the same. MCP is likely an acronym and should be kept as is unless you know the full Spanish equivalent. * **Math Agent:** "Agente Matemático" is a direct and accurate translation. * **Microsoft Word:** "Microsoft Word" remains the same, as it's a proper noun. * **Gmail:** "Gmail" remains the same, as it's a proper noun. * **Integration:** "Integración" is the standard translation. * **with:** "con" is the standard translation. "en" can also be used to mean "in" or "on" in the context of integration. **Recommendation:** I recommend using **Option 1: Servidor MCP para Agente Matemático con Integración de Microsoft Word y Gmail.** It's clear, concise, and technically accurate. If you want to emphasize the integration, **Option 3** is a good alternative.

Spider MCP

Enables web searching and webpage scraping using pure crawler technology without requiring official APIs. Supports Bing web and news search, batch webpage scraping, and content extraction through Puppeteer automation.

Yandex Tracker MCP Server

Enables AI assistants to interact with Yandex.Tracker task management system through MCP protocol. Supports creating and managing issues, searching tasks, handling comments, managing projects and queues, and generating analytics reports.

x402 MCP Payment

Enables AI agents to access paid content by integrating cryptocurrency payments through the x402 protocol, allowing LLMs to verify payments and retrieve paid resources automatically.

ALA API MCP Server

Enables interaction with the Atlas of Living Australia API to search and access 152+ million biodiversity records. Supports occurrence searches, spatial queries, bulk data downloads, and taxonomic data retrieval through natural language.

Frida Game Hacking MCP

Provides Cheat Engine-like capabilities for game hacking and reverse engineering through Frida, enabling memory scanning, value modification, pattern matching, function hooking, and code injection across processes.

Documentation Generator MCP Server

Enables AI assistants to generate professional documentation using structured templates based on the POWER framework. Provides access to standardized templates for README, architecture, API, components, and schema documentation.

MCP-Hub-MCP Server

A hub server that connects to and manages other MCP servers, allowing users to bypass Cursor's 40-tool limit and reduce AI mistakes by hiding infrequently used tools.

Azure APIM + MCP Server Demo

Exposes backend APIs through Azure API Management as an MCP server to enable AI assistants to interact with product catalogs and order systems. It provides standardized tools for searching products, retrieving details, and managing orders via the Model Context Protocol.