Discover Awesome MCP Servers

Tax Alert Chatbot MCP Server

A server that powers an interactive chatbot for querying and managing tax alerts in a SQLite database using Google Gemini models and LangGraph's REACT agent framework.

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.

Alibaba Cloud DMS MCP Server

A Model Context Protocol server that enables large language models to access database metadata and perform cross-engine data querying across diverse database ecosystems.

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.

JP's MCP Collection

A comprehensive utility MCP server that enables AI assistants to execute system commands, manage files, integrate with Google Sheets and Tasks, perform AI-powered text processing, and load dynamic prompts from markdown files.

Enterprise MCP Server

A production-ready Model Context Protocol server that integrates with ServiceNow for enterprise workflows and provides comprehensive health monitoring capabilities.

Firecrawl MCP Server

Um servidor de Protocolo de Contexto de Modelo que permite que assistentes de IA realizem web scraping, rastreamento, pesquisa e extração de dados avançados através da API Firecrawl.

Documentation MCP Server

A server that enables Claude to search and access documentation from popular libraries like LangChain, LlamaIndex, and OpenAI directly within conversations.

Docker Server Manager Go MCP

dockerServerManagerGoMCP

MSFS SDK MCP Server

A modern MCP server that provides fast, structured access to Microsoft Flight Simulator SDK documentation through natural language and structured queries.

@bldbl/mcp

This package enables AI assistants (Claude, GPT, etc.) to work directly with Buildable projects using the Model Context Protocol (MCP). AI assistants can get project context, manage tasks and track progress (for projects created at https://bldbl.dev).

Uni-res_MCP

An MCP server for my University Results

MCP Google Server

A Model Context Protocol server that provides web search capabilities using Google Custom Search API and webpage content extraction functionality.

OpenGenes MCP Server

Provides standardized access to aging and longevity research data from the OpenGenes database, enabling AI assistants to query comprehensive biomedical datasets through SQL and structured interfaces.

Ziwei Astrology MCP Server

Enables generation of detailed Chinese Ziwei Doushu (Purple Star) astrological charts with geographic location support and true solar time conversion. Provides tools for geocoding locations, converting Beijing time to apparent solar time, and creating comprehensive astrology readings based on birth information.

AI MCP ServiceNow

A Model Context Protocol server that integrates with ServiceNow instances, allowing users to utilize AI tools within ServiceNow without writing code.

mcp-searxng-tool

Um servidor MCP para permitir que Agentes de IA pesquisem conteúdo e informações de sites externos através do serviço SearXNG.

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!

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.

Notes MCP

An MCP server that enables AI assistants like Claude to access and manipulate Apple Notes on macOS, allowing for retrieving, creating, and managing notes through natural language interactions.

Audacity MCP Server

Servidor MCP para Audacity

Weather MCP Tool

A Model Context Protocol tool that provides weather information for cities, with London access requiring Solana devnet payment via the Latinum Wallet MCP server.

DART 재무제표 분석 MCP 서버

DART API를 활용하여 다중 기업의 재무제표 정보를 분석하고 시각화하는 서버로, 매출액, 당기순이익, 총자산 등 다양한 재무 지표를 차트와 대시보드로 생성합니다.

Slack MCP Server with SSE Transport

Servidor MCP do Slack com transporte SSE