Discover Awesome MCP Servers

EasyPeasyMCP

A lightweight, zero-config MCP server that makes documentation and API specifications instantly accessible to AI models using the llms.txt standard. It enables searching and retrieving full documentation, OpenAPI, and AsyncAPI specs without requiring a complex RAG infrastructure or vector database.

Roo Code Memory Bank MCP Server

Enables AI assistants to maintain persistent project context across sessions by storing and retrieving structured information in markdown files organized in a memory bank directory.

TypeScript MCP Server Boilerplate

A boilerplate project for quickly developing Model Context Protocol (MCP) servers using TypeScript SDK, with example tools (calculator, greeting) and resources (server info) included.

Humanizer APIs MCP Server

Enables access to Humanizer APIs for text humanization with multiple models including basic, easy-use, and multi-language support. Requires API key authentication.

ServiceNow MCP Server

Enables Claude to interact with ServiceNow instances through comprehensive API integration. Supports incident management, service catalog operations, change requests, knowledge base management, user administration, and agile project management with multiple authentication methods.

MCP Server Code Execution Mode

Executes Python code in isolated rootless containers while proxying MCP server tools, reducing context overhead by 95%+ and enabling complex multi-tool workflows through sandboxed code execution.

OHIP MCP Server

A universal gateway for the Oracle Hospitality Interface Platform (OHIP) that enables AI assistants to execute authenticated API requests for hotel management tasks like reservations and inventory. It features automated OAuth 2.0 token management and secure credential handling for seamless integration with Oracle Hospitality services.

Mews MCP

This MCP server provides comprehensive access to the Mews hospitality platform API, covering customer and company management, reservation operations, financial transactions, account management, configuration settings, and services inventory. It implements the core functionality needed for hospitalit

Task Manager MCP Server

Enables task management through natural language with full CRUD operations including add, list, update, complete, and delete tasks with JSON persistence.

Gmail MCP Server by CData

Gmail MCP Server by CData

Pine Script v6 MCP Server

Provides accurate Pine Script v6 coding assistance through search, completion, and reference lookup tools using a comprehensive Japanese manual database. Enables developers to quickly find functions, constants, annotations, and API specifications for TradingView Pine Script development.

Anywhere MCP Server

A Model Context Protocol server that integrates with AT&T Cybersecurity's USM Anywhere platform, providing secure access to security monitoring data including alarms, events, and threat intelligence.

AVS Document Search System

A vector search system that enables semantic retrieval of document chunks using MongoDB Atlas Vector Search and Voyage AI embeddings, allowing users to search documents by meaning rather than just keywords.

n8n-mcp

A Python-based MCP server adapted from the n8n_agent project that implements a note storage and summarization system. It enables users to create, retrieve, and summarize notes through the Model Context Protocol.

Monad NFT Launch Tool

An MCP server that helps users create NFT collections, deploy smart contracts to the Monad blockchain, and generate mint websites with Claude AI integration.

MCP Chat

A command-line interface application that enables interaction with LLMs through document retrieval, command-based prompts, and extensible tool integrations using the Model Control Protocol architecture.

GemForge-MCP

Professional Gemini API integration for Claude and MCP-compatible hosts with intelligent model selection and advanced file handling capabilities.

Azure AI Foundry MCP Server

Enables interaction with Azure AI Foundry services through a unified interface for model exploration and deployment, knowledge indexing and search, AI evaluation, and fine-tuning operations. Supports both GitHub token-based model testing and full Azure deployment workflows.

Directory Explorer MCP Server

A Model Context Protocol server that provides token-aware directory exploration and file analysis for Large Language Models, enabling intelligent codebase navigation with features like directory scanning, file content extraction, and token usage statistics.

RS.ge Waybill MCP Server

Enables natural language queries for Georgian tax system waybills through the RS.ge SOAP API. Supports waybill retrieval by date range, company TIN lookups, and access to tax system dictionaries.

Image Generator

I can't directly generate and return an image using Together.ai (or any other service) within this text-based environment. I am a language model, not an image generation tool. However, I can provide you with the information you need to do it yourself. Here's what you'd typically need to do: 1. **Access Together.ai's API:** You'll need to sign up for an account on Together.ai and obtain an API key. This key is how you authenticate your requests. 2. **Use their API documentation:** Together.ai will have documentation on how to use their image generation API. This documentation will tell you: * **The endpoint (URL) to send your request to.** * **The required parameters:** This will almost certainly include a text prompt (the description of the image you want to generate). It might also include parameters for image size, style, number of images, etc. * **The format of the request (usually JSON).** * **The format of the response:** This will likely include a URL or data representing the generated image. 3. **Write code to make the API request:** You'll need to use a programming language (like Python) and a library that can make HTTP requests (like `requests` in Python) to send the request to Together.ai's API. 4. **Process the response:** Once you get the response from Together.ai, you'll need to parse it to extract the image data or URL. 5. **Display or save the image:** Finally, you can display the image in your application or save it to a file. **Example (Conceptual Python Code - Requires Together.ai API Key and Installation of `requests`):** ```python import requests import json # Replace with your actual Together.ai API key API_KEY = "YOUR_TOGETHERAI_API_KEY" def generate_image(prompt): """Generates an image using Together.ai based on the given prompt.""" url = "THE_TOGETHERAI_IMAGE_GENERATION_ENDPOINT" # Replace with the actual endpoint headers = { "Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json" } data = { "prompt": prompt, "width": 512, # Example: Image width "height": 512 # Example: Image height # Add other parameters as needed based on Together.ai's documentation } try: response = requests.post(url, headers=headers, data=json.dumps(data)) response.raise_for_status() # Raise an exception for bad status codes (4xx or 5xx) response_json = response.json() # Assuming the response contains a URL to the image image_url = response_json.get("image_url") # Adjust based on the actual response structure if image_url: print(f"Image URL: {image_url}") # You can then download the image using requests.get(image_url) # and save it to a file. Or display it in a GUI. return image_url # Or return the image data itself if that's what the API provides else: print("Error: Image URL not found in the response.") return None except requests.exceptions.RequestException as e: print(f"Error making API request: {e}") return None except json.JSONDecodeError: print("Error: Could not decode JSON response.") return None # Example usage: prompt = "A futuristic cityscape at sunset" image_url = generate_image(prompt) if image_url: print("Image generated successfully!") # Further processing of the image (download, display, etc.) else: print("Image generation failed.") ``` **Important Considerations:** * **API Documentation is Key:** The most important thing is to carefully read and understand Together.ai's API documentation. The code above is just a general example; you'll need to adapt it to their specific requirements. * **Error Handling:** The example includes basic error handling, but you should add more robust error handling to catch potential issues like network problems, invalid API keys, or incorrect parameters. * **Rate Limits:** Be aware of Together.ai's rate limits (how many requests you can make per minute/hour). You might need to implement logic to handle rate limiting. * **Cost:** Using image generation APIs often incurs costs. Understand Together.ai's pricing model before you start using the API extensively. **Translation to Spanish (of the explanation, not the code):** No puedo generar y devolver directamente una imagen usando Together.ai (o cualquier otro servicio) dentro de este entorno basado en texto. Soy un modelo de lenguaje, no una herramienta de generación de imágenes. Sin embargo, puedo proporcionarte la información que necesitas para hacerlo tú mismo. Esto es lo que normalmente necesitarías hacer: 1. **Acceder a la API de Together.ai:** Necesitarás registrarte para obtener una cuenta en Together.ai y obtener una clave API. Esta clave es cómo autenticas tus solicitudes. 2. **Usar su documentación de la API:** Together.ai tendrá documentación sobre cómo usar su API de generación de imágenes. Esta documentación te dirá: * **El endpoint (URL) al que enviar tu solicitud.** * **Los parámetros requeridos:** Esto casi seguro que incluirá un prompt de texto (la descripción de la imagen que quieres generar). También podría incluir parámetros para el tamaño de la imagen, el estilo, el número de imágenes, etc. * **El formato de la solicitud (normalmente JSON).** * **El formato de la respuesta:** Esto probablemente incluirá una URL o datos que representen la imagen generada. 3. **Escribir código para hacer la solicitud a la API:** Necesitarás usar un lenguaje de programación (como Python) y una biblioteca que pueda hacer solicitudes HTTP (como `requests` en Python) para enviar la solicitud a la API de Together.ai. 4. **Procesar la respuesta:** Una vez que obtengas la respuesta de Together.ai, necesitarás analizarla para extraer los datos de la imagen o la URL. 5. **Mostrar o guardar la imagen:** Finalmente, puedes mostrar la imagen en tu aplicación o guardarla en un archivo. **Consideraciones importantes:** * **La documentación de la API es clave:** Lo más importante es leer y comprender cuidadosamente la documentación de la API de Together.ai. El código anterior es solo un ejemplo general; necesitarás adaptarlo a sus requisitos específicos. * **Manejo de errores:** El ejemplo incluye un manejo básico de errores, pero debes agregar un manejo de errores más robusto para detectar posibles problemas como problemas de red, claves API no válidas o parámetros incorrectos. * **Límites de velocidad:** Ten en cuenta los límites de velocidad de Together.ai (cuántas solicitudes puedes hacer por minuto/hora). Es posible que debas implementar lógica para manejar la limitación de velocidad. * **Costo:** El uso de las API de generación de imágenes a menudo incurre en costos. Comprende el modelo de precios de Together.ai antes de comenzar a usar la API extensivamente.

Postgres MCP Pro

An open-source MCP server that provides AI agents with advanced PostgreSQL capabilities including index tuning, query plan optimization, and comprehensive database health analysis. It supports safe SQL execution through configurable access modes and offers both stdio and SSE transport options for various development environments.

nova-act-mcp

Un servidor MCP que proporciona herramientas para controlar navegadores web utilizando el SDK Amazon Nova Act. Permite flujos de trabajo de automatización de navegadores de varios pasos a través de agentes MCP.

claude-database-tools

A SQL Server CLI and MCP server for Claude Code that supports standard SQL Server authentication for database interaction and management. It enables users to perform schema exploration, execute queries, and manage data records with built-in SQL injection prevention.

crw-mcp

Web scraping, crawling, and structured data extraction for AI agents. 5 tools: scrape (clean markdown from any URL), crawl (entire sites), map (discover URLs), extract (structured JSON), and search. 833ms avg latency, single binary, self-hostable.

Spotify MCP Server

Enables control of Spotify playback and management of liked songs through natural language. Supports play/pause controls, track information retrieval, and adding songs to your Spotify library.

MCP OpenFoodTox

Enables natural language querying of EFSA's OpenFoodTox database containing 8,006 chemical substances with toxicity data, safety assessments, risk limits, and genotoxicity studies for food additives, pesticides, and contaminants.