Discover Awesome MCP Servers

Limetest MCP Server

Enables automated end-to-end testing powered by Playwright where test cases are defined in natural language and executed by AI. Uses lightweight snapshot analysis with vision mode fallback for sophisticated testing scenarios.

FastMCP Boilerplate

A starter template for building MCP servers with FastMCP, providing testing, linting, formatting, and NPM publishing setup.

SSE MCP Server with JWT Authentication

Um servidor de Protocolo de Contexto de Modelo que expõe múltiplas ferramentas de IA através de transporte SSE com autenticação segura baseada em JWT, permitindo o registro dinâmico de ferramentas e gerenciamento de sessão.

Asana MCP Server

An MCP (Multi-Agent Conversation Protocol) server that enables interacting with the Asana API through natural language commands for task management, project organization, and team collaboration.

Memory MCP Worker

Provides cross-device access to a persistent knowledge graph via Cloudflare Workers, enabling memory storage and retrieval through both MCP protocol and REST API with full-text search capabilities.

MCP Game Helper

Servidor de Protocolo de Contexto de Modelo Personalizado (MCP) que fornece ferramentas baseadas em IA para auxiliar desenvolvedores de jogos em tarefas relacionadas a balanceamento de combate, análise de habilidades, ritmo de níveis e simulação.

MCP Server with External Tools

Enables AI models to access external services including weather data, file system operations, and SQLite database interactions through a standardized JSON-RPC interface. Features production-ready architecture with security, rate limiting, and comprehensive error handling.

Personal Knowledge Assistant

Manages and analyzes personal information across email, social media, documents, and productivity metrics with AI-powered insights, communication pattern analysis, and cross-platform content management.

local-fetch

A powerful MCP server that offers web scraping capabilities unrestricted by robots.txt, supports multiple HTTP methods and custom request Settings.

Confluence MCP Server

Espelho de

Data Dictionary MCP

Um servidor de Protocolo de Contexto de Modelo (MCP) que coordena agentes de IA para transformar tabelas de banco de dados em dicionários de dados no estilo da Wikipédia.

Me-MCP

A personal MCP Server that allows AI agents to retrieve your resume and contact you through Discord webhooks, deployable via Cloudflare Workers.

Todoist MCP

Connects LLMs to Todoist for comprehensive task and project management, enabling natural language task creation, updates, collaboration, and productivity tracking through the Todoist API.

Expo MCP Server

A Model Context Protocol server designed to streamline Expo and React Native development for AI assistants like Cursor and Claude. It provides a comprehensive suite of tools for project initialization, EAS builds, OTA updates, and development server management.

Godot MCP Server

An MCP server that provides tools for Godot Engine development, enabling users to run unit tests, check for syntax errors, and manage project scenes or exports.

OrigeneMCP

An advanced integrated MCP server platform that combines 600+ tools and multiple biomedical databases to enable comprehensive information retrieval across molecules, proteins, genes, and diseases for accelerating therapeutic research.

Fizzy MCP Server

Enables AI assistants to interact with Fizzy project management boards, cards, and tasks through natural language. It provides full API coverage for managing project workflows, comments, and notifications across multiple transport protocols and IDEs.

MCP E-commerce Demo

A Laravel-based Model Context Protocol demonstration that enables users to manage orders and query e-commerce data in Traditional Chinese through an AI-powered chat interface.

Iteration MCP

Automates iteration management and code review submission workflows with DingTalk authentication. Supports interactive 5-step iteration creation, Git integration, smart time estimation, and automatic submission to company CodeReview systems.

MCP-Audio Plugin

A voice-to-text transcription service that converts audio files to transcripts using SiliconFlow, supporting both multipart/form-data and base64 formats.

Formath MCP

Enables extraction of mathematical content from TeX papers and conversion to Lean code through a structured intermediate representation. Supports project scaffolding, entity management, and task tracking for mathematical formalization workflows.

Polyagent MCP

Enables any MCP-compatible client to use existing Claude Code agents from .claude/agents/ directories. Spawns agents in separate CLI sessions for better context optimization and performance across Codex, Gemini CLI, and other AI coding assistants.

Argo Workflow MCP Server

Enables AI agents to manage Argo Workflows through REST API, supporting workflow template and instance operations including creation, submission, monitoring, and deletion with token authentication.

mcp-flyin

A server that handles messaging or commands over a custom protocol

Azure Model Context Protocol (MCP) Hub

Okay, here's a breakdown of resources, tools, and samples for building and integrating Model Context Protocol (MCP) servers on Azure, considering multiple languages. Since MCP is relatively new, direct "MCP on Azure" specific resources are limited. I'll focus on the underlying technologies and how to adapt them. **Understanding the Landscape** * **Model Context Protocol (MCP):** MCP is a protocol designed to provide context to Large Language Models (LLMs). It allows you to pass structured data, metadata, and other relevant information to the LLM along with the user's query. This helps the LLM provide more accurate, relevant, and personalized responses. Think of it as a way to give the LLM a "memory" or "knowledge base" for a specific conversation or task. * **Azure's Role:** Azure provides the infrastructure and services to host and manage your MCP server. This includes compute (Virtual Machines, Azure Container Apps, Azure Functions), data storage (Azure Cosmos DB, Azure SQL Database, Azure Blob Storage), and API management (Azure API Management). * **Key Technologies:** * **API Development:** You'll need to build an API endpoint that receives requests (likely HTTP), processes the MCP data, interacts with your LLM, and returns a response. * **Data Storage:** You'll likely need a database or storage solution to store the context data that your MCP server uses. * **LLM Integration:** You'll need to integrate with an LLM service, such as Azure OpenAI Service, or a locally hosted LLM. * **Authentication/Authorization:** Securing your API is crucial. Azure Active Directory (Azure AD) is a common choice. **Resources and Tools (with Portuguese considerations):** 1. **General Azure API Development (Foundation):** * **Azure Functions:** A serverless compute service that's great for building APIs. Supports multiple languages. * **Languages:** C#, Python, JavaScript/TypeScript, Java, PowerShell, Go. * **Documentation:** * English: [https://learn.microsoft.com/en-us/azure/azure-functions/](https://learn.microsoft.com/en-us/azure/azure-functions/) * Portuguese: Search for "Azure Functions documentação" on Microsoft Learn. While not all content is directly translated, you'll find many articles and tutorials in Portuguese. Use the language selector on the Microsoft Learn site. * **Samples:** Look for HTTP trigger function examples in your chosen language. Adapt these to handle MCP data. * **Azure App Service:** A platform for hosting web applications and APIs. More control than Functions, but requires more management. * **Languages:** Supports a wide range of languages and frameworks (e.g., .NET, Python (Flask, Django), Node.js, Java (Spring Boot)). * **Documentation:** * English: [https://learn.microsoft.com/en-us/azure/app-service/](https://learn.microsoft.com/en-us/azure/app-service/) * Portuguese: Search for "Azure App Service documentação" on Microsoft Learn. * **Samples:** Look for examples of building REST APIs with your chosen framework and deploying them to Azure App Service. * **Azure Container Apps:** A serverless container execution service. Ideal if you're using Docker containers. * **Languages:** Any language that can be containerized. * **Documentation:** * English: [https://learn.microsoft.com/en-us/azure/container-apps/](https://learn.microsoft.com/en-us/azure/container-apps/) * Portuguese: Search for "Azure Container Apps documentação" on Microsoft Learn. * **Samples:** Focus on containerizing your API application (e.g., a Flask API in Python) and deploying it to Azure Container Apps. 2. **Data Storage:** * **Azure Cosmos DB:** A NoSQL database service. Good for flexible data schemas. * **Documentation:** * English: [https://learn.microsoft.com/en-us/azure/cosmos-db/](https://learn.microsoft.com/en-us/azure/cosmos-db/) * Portuguese: Search for "Azure Cosmos DB documentação" on Microsoft Learn. * **Samples:** Look for examples of connecting to Cosmos DB from your chosen language (e.g., Python, .NET) and performing CRUD operations. * **Azure SQL Database:** A relational database service. Good for structured data. * **Documentation:** * English: [https://learn.microsoft.com/en-us/azure/azure-sql/database/](https://learn.microsoft.com/en-us/azure/azure-sql/database/) * Portuguese: Search for "Azure SQL Database documentação" on Microsoft Learn. * **Samples:** Look for examples of connecting to Azure SQL Database from your chosen language. * **Azure Blob Storage:** Object storage for unstructured data (e.g., documents, images). * **Documentation:** * English: [https://learn.microsoft.com/en-us/azure/storage/blobs/](https://learn.microsoft.com/en-us/azure/storage/blobs/) * Portuguese: Search for "Azure Blob Storage documentação" on Microsoft Learn. 3. **LLM Integration (Azure OpenAI Service):** * **Azure OpenAI Service:** Provides access to OpenAI's models (GPT-3, GPT-4, etc.). * **Documentation:** * English: [https://learn.microsoft.com/en-us/azure/cognitive-services/openai/](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/) * Portuguese: Search for "Azure OpenAI Service documentação" on Microsoft Learn. * **Samples:** The Azure OpenAI Service documentation has code samples in Python and C# for interacting with the models. Adapt these to incorporate the context data from your MCP server. * **Langchain:** A framework that simplifies working with LLMs. It has integrations for Azure OpenAI Service and various data sources. * **Documentation:** [https://www.langchain.com/](https://www.langchain.com/) * **Languages:** Primarily Python. * **Note:** While Langchain's documentation might not be directly in Portuguese, the Python code itself is language-agnostic. You can find community tutorials and examples in Portuguese. 4. **API Management:** * **Azure API Management:** A service for managing and securing your APIs. * **Documentation:** * English: [https://learn.microsoft.com/en-us/azure/api-management/](https://learn.microsoft.com/en-us/azure/api-management/) * Portuguese: Search for "Azure API Management documentação" on Microsoft Learn. 5. **Authentication/Authorization:** * **Azure Active Directory (Azure AD):** Microsoft's cloud-based identity and access management service. * **Documentation:** * English: [https://learn.microsoft.com/en-us/azure/active-directory/](https://learn.microsoft.com/en-us/azure/active-directory/) * Portuguese: Search for "Azure Active Directory documentação" on Microsoft Learn. **Example Scenario (Python with Flask and Azure OpenAI):** 1. **Create a Flask API:** Build a simple Flask API endpoint that receives HTTP requests. 2. **Parse MCP Data:** The API endpoint parses the incoming request body, extracting the MCP data (e.g., user ID, conversation history, relevant documents). You'll need to define the structure of your MCP data. 3. **Retrieve Context:** Based on the MCP data, retrieve relevant context information from Azure Cosmos DB (or another data store). 4. **Format Prompt:** Combine the user's query with the retrieved context to create a prompt for the LLM. This is a crucial step for effective context injection. 5. **Call Azure OpenAI:** Use the Azure OpenAI Service Python SDK to send the prompt to the LLM. 6. **Return Response:** Return the LLM's response to the client. 7. **Deploy to Azure:** Deploy the Flask API to Azure App Service or Azure Container Apps. 8. **Secure with Azure AD:** Integrate Azure AD for authentication and authorization. **Code Snippet (Python - Flask Example):** ```python from flask import Flask, request, jsonify import openai import os app = Flask(__name__) # Configure Azure OpenAI openai.api_type = "azure" openai.api_base = os.getenv("AZURE_OPENAI_ENDPOINT") openai.api_version = "2023-05-15" # Or your API version openai.api_key = os.getenv("AZURE_OPENAI_KEY") model_name = "your-deployment-name" # Replace with your deployment name # Mock function to retrieve context (replace with your Cosmos DB logic) def get_context(user_id): # In a real application, you'd query your database here if user_id == "123": return "User is a software engineer specializing in Python." else: return "No context available for this user." @app.route('/mcp', methods=['POST']) def mcp_endpoint(): try: data = request.get_json() user_query = data.get('query') user_id = data.get('user_id') # Example MCP data context = get_context(user_id) prompt = f"Context: {context}\nUser Query: {user_query}\nResponse:" response = openai.Completion.create( engine=model_name, prompt=prompt, max_tokens=150, n=1, stop=None, temperature=0.7, ) llm_response = response.choices[0].text.strip() return jsonify({'response': llm_response}) except Exception as e: return jsonify({'error': str(e)}), 500 if __name__ == '__main__': app.run(debug=True) ``` **Key Considerations for Portuguese:** * **LLM Language Support:** Ensure that the LLM you're using (e.g., Azure OpenAI) has good support for the Portuguese language. Test its performance with Portuguese prompts and context. * **Data Storage Encoding:** Use UTF-8 encoding for your data storage to properly handle Portuguese characters (accent marks, etc.). * **Natural Language Processing (NLP) Libraries:** Consider using NLP libraries that support Portuguese for tasks like tokenization, stemming, and sentiment analysis if you need to process the context data. Examples include: * **spaCy:** Has Portuguese language models. * **NLTK:** Supports Portuguese. * **Translation Services:** If your context data is in English, you might need to use a translation service (e.g., Azure Translator) to translate it to Portuguese before sending it to the LLM. However, this can add latency and complexity. **Steps to Build Your MCP Server:** 1. **Define your MCP data structure:** What information will you pass to the LLM? 2. **Choose your language and framework:** Python (Flask/FastAPI), .NET, Node.js are good choices. 3. **Set up your Azure environment:** Create an Azure account, provision the necessary resources (App Service, Cosmos DB, Azure OpenAI). 4. **Develop your API:** Implement the API endpoint to receive MCP data, retrieve context, format the prompt, and call the LLM. 5. **Test thoroughly:** Test your API with different MCP data and user queries. 6. **Deploy to Azure:** Deploy your API to Azure App Service or Azure Container Apps. 7. **Monitor and optimize:** Monitor the performance of your API and optimize it as needed. **Important Notes:** * **Security:** Implement proper authentication and authorization to protect your API. * **Error Handling:** Implement robust error handling to gracefully handle unexpected errors. * **Logging:** Log important events to help you debug and monitor your API. * **Scalability:** Design your API to be scalable to handle a large number of requests. Consider using Azure's autoscaling features. * **Cost Optimization:** Monitor your Azure resource usage and optimize your configuration to minimize costs. By combining these resources and adapting them to your specific needs, you can build a robust and effective MCP server on Azure using multiple languages. Remember to focus on the core principles of API development, data storage, and LLM integration. Good luck!

macOS MCP Servers

Enables Claude Desktop and GitHub Copilot to interact with native macOS applications including Spotify, Apple Music, Notes, Calendar, FaceTime, and Contacts through natural language commands. Provides comprehensive control over music playback, note management, calendar events, video calls, and contact operations using AppleScript integration.

ClinicalTrials.gov MCP Server

Empowers AI agents with direct access to the official ClinicalTrials.gov database, enabling programmatic searching, retrieval, and analysis of clinical study data through a Model Context Protocol interface.