Discover Awesome MCP Servers

Zendesk MCP Server by CData

Zendesk MCP Server by CData

Karenina MCP

Enables natural language querying of Karenina benchmark verification results stored in SQLite databases, allowing AI assistants to explore and analyze model performance data without writing SQL manually.

guxi

webgl流体模拟

Agricultural AI MCP Server

Provides real-time crop price data from Indian government sources and agricultural web search capabilities. Enables AI chatbots to access comprehensive agricultural market information and news for farming-related queries.

Search Tools MCP Server

Enables intelligent code analysis and search across repositories using the CodeRank algorithm (inspired by PageRank) to identify critical modules, trace dependencies, find code hotspots, and perform context-aware keyword searches with importance-ranked results.

ph-civic-data-mcp

An MCP server providing real-time access to Philippine government data including earthquakes, weather, typhoons, procurement, population, and air quality. It enables AI agents to query multiple official Philippine data sources through a unified interface for research and analysis purposes.

MSFConsole MCP Server

Enables AI assistants to interact with Metasploit Framework through 28 comprehensive tools for penetration testing and security analysis. Provides secure, structured access to MSF modules, database operations, session management, and payload generation capabilities.

EGX-Data-MCP-Server

Servidor MCP para herramientas bursátiles de la Bolsa de Valores de Egipto (EGX)

GitHub MCP Server

Enables comprehensive GitHub workflow automation including Actions monitoring, PR management, code search, file operations, and repository management through a code-first architecture that reduces token usage by 98%.

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.

SQLite MCP Server

Enables comprehensive SQLite database operations including CRUD operations, schema management, and meta commands across multiple database files through the Model Context Protocol.

gdb-mcp

Enables AI assistants to control GDB debugger via MCP protocol for local and remote debugging, supporting CTF Pwn, crash analysis, and ELF inspection.

GemForge-MCP

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

MCP Beget

An MCP server for managing Beget hosting services including sites, domains, databases, and mail directly through Claude Code. It provides comprehensive tools for configuring DNS, FTP, Cron jobs, and managing backups via the Beget API.

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.

Supabase MCP Server on Phala Cloud

Servidor MCP de Supabase remoto en Phala Cloud

tavily-mcp-python

Tavily MCP Server implementation that uses fastmcp and supports both sse and stdio transports. It also supports more up to date functionalities of Tavily.

linux-mcp-server

Soporte para ejecutar comandos shell en Linux.

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.

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.

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.

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.

FoundryVTT MCP Server

Integrates with FoundryVTT tabletop gaming sessions, allowing AI assistants to query game data, roll dice, generate content (NPCs, loot, encounters), manage combat, and provide tactical suggestions through natural language.

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.

RAGBrain MCP

Connects Claude Desktop to a RAGBrain knowledge base to enable semantic search, document retrieval, and namespace management. It allows users to browse collections, discover documents by topic, and access full text content through natural language.

Rime MCP

Un servidor de Protocolo de Contexto de Modelo que permite a los modelos de IA generar y reproducir audio de texto a voz de alta calidad a través del sistema de audio nativo de tu dispositivo utilizando la API de síntesis de voz de Rime.

RefundYourSOL

Solana wallet cleanup, token trading on 12+ DEXes, and market data. Scan wallets for reclaimable SOL, close empty token accounts, burn dust tokens, buy/sell with Jito MEV protection. 7 tools for AI agents.