Discover Awesome MCP Servers

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.

unreal-editor-mcp

Provides build diagnostics and editor log tools for Unreal Engine AI development, enabling Live Coding builds, parsing compile errors, searching/filtering logs, and crash context.

mcp-edd

MCP server for Easy Digital Downloads REST API, enabling access to sales data, customers, products, and analytics from your EDD store.

proofpoint-mcp

MCP server for Proofpoint Email Protection - email security, threat intelligence, TAP (Targeted Attack Protection), and email filtering API integration

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 for building and integrating Model Context Protocol (MCP) servers on Azure using multiple languages. Since MCP is a relatively new and evolving area, direct, comprehensive "one-stop-shop" resources are still emerging. I'll provide the best available information, focusing on the core components and how to adapt them to different languages on Azure. **Understanding Model Context Protocol (MCP)** * **Core Concept:** MCP is designed to provide a standardized way for AI models to access contextual information (e.g., user data, environment data, session history) at runtime. This allows models to make more informed and personalized decisions. * **Key Components:** * **MCP Server:** The central component that manages and serves the contextual data. This is what you'll be building. * **MCP Client:** The code within your AI model or application that requests data from the MCP Server. * **Data Sources:** The systems that hold the contextual information (databases, APIs, caches, etc.). **General Approach for Building an MCP Server on Azure** 1. **Choose a Language/Framework:** Select a language and framework suitable for building a web API. Popular choices include: * **Python (with Flask or FastAPI):** Excellent for rapid development and has a large ecosystem of libraries. * **C# (.NET):** Strong performance, well-suited for enterprise applications, and integrates seamlessly with Azure services. * **Node.js (with Express):** Good for building scalable and real-time applications. * **Java (with Spring Boot):** Another robust option for enterprise-grade solutions. 2. **Define the MCP API:** Design the API endpoints that your MCP Server will expose. This will likely involve: * **Request Format:** How the client will request data (e.g., using a specific ID or set of parameters). JSON is a common choice. * **Response Format:** The structure of the data returned by the server (again, likely JSON). * **Authentication/Authorization:** How you'll secure the API to ensure only authorized clients can access the data. 3. **Implement the API Logic:** Write the code to: * Receive requests. * Fetch data from the appropriate data sources. * Transform the data into the required response format. * Handle errors gracefully. 4. **Deploy to Azure:** Choose an Azure service to host your MCP Server: * **Azure App Service:** A fully managed platform for hosting web applications. Good for most scenarios. * **Azure Functions:** Serverless compute, ideal for event-driven architectures or APIs with infrequent usage. * **Azure Kubernetes Service (AKS):** For more complex deployments requiring container orchestration. * **Azure Container Apps:** A serverless container service that simplifies deploying containerized applications. 5. **Secure the API:** Implement authentication and authorization. Options include: * **Azure Active Directory (Azure AD):** For enterprise identity management. * **API Keys:** A simpler approach for less sensitive data. * **Managed Identities:** Allow your Azure resources to authenticate to other Azure services without needing to manage credentials. 6. **Monitor and Log:** Use Azure Monitor to track the performance and health of your MCP Server. Implement logging to help diagnose issues. **Language-Specific Resources and Examples (Adaptable for MCP)** While direct MCP examples in multiple languages are scarce, you can adapt existing Azure API examples: * **Python (Flask/FastAPI):** * **Azure App Service with Python:** [https://learn.microsoft.com/en-us/azure/app-service/quickstart-python](https://learn.microsoft.com/en-us/azure/app-service/quickstart-python) * **Azure Functions with Python:** [https://learn.microsoft.com/en-us/azure/azure-functions/functions-create-first-function-python](https://learn.microsoft.com/en-us/azure/azure-functions/functions-create-first-function-python) * **Example (Conceptual):** You would adapt these examples to: * Define API endpoints for your MCP data requests (e.g., `/context/{user_id}`). * Fetch data from your data sources (e.g., Azure Cosmos DB, Azure SQL Database). * Return the data in a JSON format. * **C# (.NET):** * **Azure App Service with .NET:** [https://learn.microsoft.com/en-us/azure/app-service/quickstart-dotnetcore](https://learn.microsoft.com/en-us/azure/app-service/quickstart-dotnetcore) * **Azure Functions with C#:** [https://learn.microsoft.com/en-us/azure/azure-functions/functions-create-first-function-vs](https://learn.microsoft.com/en-us/azure/azure-functions/functions-create-first-function-vs) * **Example (Conceptual):** Similar to Python, you'd create API controllers to handle MCP requests, access data sources using Entity Framework or other data access libraries, and return JSON responses. * **Node.js (Express):** * **Azure App Service with Node.js:** [https://learn.microsoft.com/en-us/azure/app-service/quickstart-nodejs](https://learn.microsoft.com/en-us/azure/app-service/quickstart-nodejs) * **Azure Functions with Node.js:** [https://learn.microsoft.com/en-us/azure/azure-functions/functions-create-first-function-node](https://learn.microsoft.com/en-us/azure/azure-functions/functions-create-first-function-node) * **Example (Conceptual):** Use Express to define routes for your MCP API, connect to data sources using libraries like `pg` (for PostgreSQL) or `mongodb` (for MongoDB), and return JSON data. * **Java (Spring Boot):** * **Azure App Service with Java:** [https://learn.microsoft.com/en-us/azure/app-service/quickstart-java](https://learn.microsoft.com/en-us/azure/app-service/quickstart-java) * **Azure Functions with Java:** [https://learn.microsoft.com/en-us/azure/azure-functions/functions-create-first-java](https://learn.microsoft.com/en-us/azure/azure-functions/functions-create-first-java) * **Example (Conceptual):** Use Spring Boot's REST controller features to create API endpoints, use Spring Data JPA or JDBC to access databases, and return JSON responses. **Key Considerations for MCP Implementation** * **Data Consistency:** Ensure that the data served by your MCP Server is consistent and up-to-date. Consider using caching mechanisms (e.g., Azure Cache for Redis) to improve performance and reduce load on your data sources. * **Scalability:** Design your MCP Server to handle a large number of requests. Azure App Service and Azure Functions can scale automatically. For more demanding workloads, consider AKS. * **Latency:** Minimize the latency of data retrieval. Optimize your data queries and use caching effectively. Consider the geographic location of your MCP Server and your AI models. * **Security:** Protect the data served by your MCP Server. Use strong authentication and authorization mechanisms. Encrypt data in transit and at rest. * **Data Governance:** Implement policies to ensure that data is used responsibly and ethically. Comply with relevant data privacy regulations (e.g., GDPR, CCPA). **Example Scenario (Python with FastAPI on Azure App Service)** 1. **FastAPI App:** ```python from fastapi import FastAPI, HTTPException from azure.cosmos import CosmosClient, PartitionKey from typing import Optional import os app = FastAPI() # Azure Cosmos DB Configuration (replace with your actual values) COSMOS_ENDPOINT = os.environ["COSMOS_ENDPOINT"] COSMOS_KEY = os.environ["COSMOS_KEY"] DATABASE_NAME = "mcp_db" CONTAINER_NAME = "user_context" # Initialize Cosmos DB client cosmos_client = CosmosClient(COSMOS_ENDPOINT, COSMOS_KEY) database = cosmos_client.get_database_client(DATABASE_NAME) container = database.get_container_client(CONTAINER_NAME) @app.get("/context/{user_id}") async def get_user_context(user_id: str): """ Retrieves user context data from Cosmos DB. """ try: item = container.read_item(item=user_id, partition_key=user_id) return item except Exception as e: raise HTTPException(status_code=404, detail="User context not found") @app.get("/health") async def health_check(): return {"status": "ok"} ``` 2. **Deployment to Azure App Service:** * Create an Azure App Service instance. * Configure environment variables for `COSMOS_ENDPOINT` and `COSMOS_KEY`. * Deploy the Python code to the App Service. You'll likely need a `requirements.txt` file listing dependencies (e.g., `fastapi`, `azure-cosmos`). **Important Notes:** * **MCP is Evolving:** The Model Context Protocol is still under development. Expect changes and updates to the specifications and available tools. * **Customization is Key:** You'll need to tailor your MCP Server to the specific needs of your AI models and data sources. * **Security Best Practices:** Always prioritize security when building and deploying your MCP Server. I hope this comprehensive guide helps you get started with building and integrating MCP servers on Azure using multiple languages! Remember to adapt the examples and resources to your specific requirements.

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.

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.

Audacity MCP Server

Audacity 的 MCP 服务器

artl-mcp

Enables comprehensive scientific literature retrieval and analysis through Europe PMC, PubMed, and other databases, supporting metadata extraction, full-text access, and identifier conversion via MCP and CLI.

cleanshot-mcp

Enables AI assistants to control CleanShot X for screenshots, recordings, OCR, and annotations via natural language commands.

Kreato MCP Server

Enables AI agents to perform Web3 creator monetization actions like tipping creators, buying digital products, and managing memberships through natural language.

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.

Remote MCP Server (Authless)

A template for deploying authentication-free MCP servers on Cloudflare Workers that can be accessed remotely from clients like Claude Desktop or the Cloudflare AI Playground.

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.

Origin Pro MCP Server

Enables AI assistants to control OriginLab Origin Pro via COM automation for data analysis, graphing, and styling, with real-time GUI updates.

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

这个仓库包含了我对 MCP 服务器的实验。

ourgroceries-mcp

Manage grocery lists on OurGroceries.com via CLI or MCP, supporting item operations, list management, and natural-language resolution.

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.

Me-MCP

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

AI List My Business

Country-agnostic MCP-callable directory for AI agents to find local SMBs — realtors, insurance agents, medical practitioners — by category, location, or natural-language query. Returns business catalog data and UTM-tagged booking URLs (zero PII).