Discover Awesome MCP Servers

chinchillo-mcp-server

チンチロができるMCPサーバーです。

Draw-it-MCP

A browser-based drawing application with AI integration that enables Claude to analyze artwork, provide feedback on composition and techniques, and discuss artistic concepts through MCP tools.

swanlab-mcp

Enables read-only access to SwanLab workspaces, projects, runs, and metrics through the Model Context Protocol.

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.

kaseya-vsa-mcp

MCP server for Kaseya VSA — endpoints, patches, procedures, alarms, and tickets. Enables AI assistants to manage and monitor devices via the Kaseya VSA RMM platform.

AI MCP ServiceNow

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

xmlriver-mcp

MCP server for XMLRiver enabling Google and Yandex SERP parsing, Yandex Wordstat keyword frequency, indexing checks, and account operations.

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.

Mattermost S MCP

Enables sending messages to Mattermost channels through webhooks via MCP protocol. Supports multiple webhook channels with simple YAML configuration and integrates seamlessly with Claude Desktop.

Contraption Company MCP

Provides semantic search and access to Contraption Company blog posts and essays. Uses AI-powered embeddings to find relevant content and supports real-time updates via webhooks.

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.

Cloudways MCP Server

MCP server for Cloudways enabling server management, app deployment, backups, SSL, environment variables, and monitoring via Claude Code.

Expo Docs MCP Server

Enables AI-powered semantic search through Expo SDK documentation across multiple versions (v51-v53 and latest), allowing developers to quickly find relevant documentation with configurable similarity scoring.

Weather MCP Server

Connects Claude Desktop to the Open-Meteo API to retrieve real-time weather data for cities worldwide without requiring an API key.

Fresh Jots MCP Server

This server connects Fresh Jots notes with clients

ClickUp MCP Server

Enables natural language management of ClickUp workspaces, including task CRUD operations, task listing, and user profile retrieval via Claude Desktop.

newrelic-mcp

A command-line tool for monitoring and analyzing New Relic application metrics using MCP.

wapimaji-mcp

MCP server exposing Kenya NDMA drought phase classifications across all 47 counties, with tools for structured data access and SMS-based alerting via Africa’s Talking.

mcp-calendly

Enables interaction with Calendly to manage event types, scheduled events, and invitees. It provides tools for checking user availability and canceling appointments directly through the Calendly API.

endnote-mcp

Connect your EndNote reference library to Claude AI. Search references, read PDFs, format citations, find related papers, and generate bibliographies directly in Claude Desktop conversations.

mcp-servers-experiments

Este repositório contém meus experimentos com servidores MCP.

MCP RAG Server

Enables retrieval-augmented generation (RAG) by indexing and searching through documents (Markdown, text, PowerPoint, PDF) using vector embeddings with multilingual-e5-large model and PostgreSQL pgvector. Supports contextual chunk retrieval and incremental indexing for efficient document management.

mcp_poc

A PDF-to-Markdown converter built with the Model-View-Controller (MVC) pattern using the Model Context Protocol (MCP).

Mcpserver

Um servidor MCP de exemplo.

MCP Atlassian

A Model Context Protocol server for Atlassian Jira and Confluence that supports both Cloud and On-Prem/Data Center deployments. It enables AI assistants to search, create, and manage issues and pages using secure authentication methods like PAT and OAuth.