Discover Awesome MCP Servers

Timezone By Location MCP Server

Provides access to the Timezone By Location API to retrieve timezone information based on geographic location data.

Resemble AI Voice Generation MCP Server

Integra-se com Claude e Cursor usando o Protocolo de Contexto do Modelo para gerar áudio de voz a partir de texto usando as vozes da Resemble AI.

MCP Hub

An Express server implementation of Model Context Protocol that allows websites to connect to LLMs through streamable HTTP and stdio transports, with a built-in chat UI for testing responses.

webdev-mcp

An MCP server that provides web development tools including taking screenshots of screens, enabling AI agents to capture and analyze visual content during development.

TypeScript MCP Server Boilerplate

A boilerplate project for quickly developing Model Context Protocol servers using TypeScript, featuring example tools (calculator, greeting) and resources (server info) with Zod schema validation.

MCP-Soccerdata

Um servidor MCP de código aberto que se conecta à SoccerDataAPI para fornecer informações atualizadas sobre partidas de futebol por meio de interações em linguagem natural.

MySQL MCP Server Pro

Provides comprehensive MySQL database operations including CRUD, performance optimization, health analysis, and anomaly detection. Supports multiple connection modes, OAuth2.0 authentication, and role-based permissions for database management through natural language.

Playwright MCP Server

A minimal server that exposes Playwright browser automation capabilities through a simple API, enabling webpage interaction, DOM manipulation, and content extraction via the Model Context Protocol.

Quark Auto-Save MCP Server

Integrates with the quark-auto-save service to automate file saving from Quark Cloud Drive shares. It enables users to manage auto-save tasks, update configurations, and trigger immediate file transfers through natural language.

MCP Server

A centralized architecture that serves as the only data access layer between frontend/backend applications and Supabase/Redis, enforcing strict data access control while maintaining simplicity and efficiency.

SMS.ir MCP Server

Servidor MCP para serviços de mensagens SMS.ir

Expense Tracker MCP Server

Enables AI assistants to manage personal expenses through natural conversation, supporting expense tracking, categorization, filtering, and financial summaries. Uses SQLite database to store expense records with full CRUD operations for comprehensive personal finance management.

Sonarr MCP Server

Enables AI assistants to manage TV series collections through Sonarr's API using natural language interactions. Supports searching, adding, updating, and deleting TV series with detailed control over quality profiles, season monitoring, and episode downloads.

FastMCP

A Model Context Protocol server that bridges MCP clients with local LLM services, enabling seamless integration with MCP-compatible applications through standard tools like chat completion, model listing, and health checks.

propresenter-mcp

A comprehensive MCP server providing full control over ProPresenter presentations, media, playlists, and stage displays via the ProPresenter API. It implements over 200 endpoints to enable detailed automation and interaction through natural language commands.

Memory MCP Server

SQLite-backed memory storage for MCP agents with optional semantic search via OpenAI embeddings, enabling agents to remember, recall, and manage contextual information across sessions.

MCP Home Assistant

Enables natural language control of Home Assistant smart home devices through Cursor AI, supporting entity queries, automation management, configuration file editing, and system operations.

MCP Server Fichador

A Model Context Protocol server that searches educational articles from todamateria.com.br and automatically creates structured reading cards with summaries, key points, and citations.

Uni-res_MCP

An MCP server for my University Results

Multilead Open API MCP Server

Enables AI assistants to interact with the Multilead platform for lead management, email campaigns, conversations, webhooks, and analytics through 74 API endpoints.

Spring AI MCP Server Example

Projeto de servidor Spring AI MCP de amostra construído por diversão e experimentação. 🚀 Implementa operações CRUD básicas usando um armazenamento de dados em memória com dados fictícios de Pessoa. 🤖

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.

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.

Blogger MCP Server

Enables AI assistants to interact with the Google Blogger API v3 to manage blog posts and metadata. It supports the full post lifecycle including creating, updating, publishing, and deleting content through natural language.

Chicken Business Management MCP Server

Enables real-time voice-to-text order processing and chicken business management through WebSocket connections and REST APIs. Supports inventory tracking, sales parsing, stock forecasting, and note collection with AI-powered transcript correction and structured data extraction.

Audacity MCP Server

Servidor MCP para Audacity