Discover Awesome MCP Servers

travel-planner

Enables travel planning through natural language, providing weather forecasts, attraction search, itinerary generation, and distance calculations using free APIs.

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.

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.

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.

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.

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.

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.

web-search-agent

An MCP server that enables Claude Code to perform web searches via Bing, read webpage content, and get current time, with a smart search skill for structured multi-source verification.

skillfinder-mcp

An MCP server that provides on-demand skill discovery for AI coding agents by querying GitHub repositories, using BM25 search to return relevant SKILL.md content.

mcp-stdio-to-streamable-http-adapter

Bridges STDIO MCP clients to Streamable HTTP MCP servers, allowing any STDIO-supported client to use Streamable HTTP servers immediately.

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.

GitHub MCP Agent Server

MCP server that exposes GitHub operations as tools for AI agents, enabling code search, issue management, and PR review.

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.

geometra

Enables AI agents to interact with UIs via semantic geometry, filling forms and navigating websites without screenshots.

mcp-weather-server

Provides real-time weather, 5-day forecasts, and weather alerts for US states via NWS and WeatherAPI.com, accessible through natural language queries.

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.

mcp-guard

Adds security capabilities like port scanning, TLS inspection, DNS enumeration, process monitoring, secrets scanning, HTTP header auditing, and CVE checking to Claude Code and Cursor.

Everything Search MCP Server

使用特定于平台的技术，在 Windows、macOS 和 Linux 操作系统上提供快速的文件搜索功能。

limitless-ai-mcp-server

Enables AI assistants to interact with Limitless AI Pendant recordings, search, retrieve, and analyze lifelogs via the Model Context Protocol.

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.

QWED-MCP

Enables deterministic verification for AI assistants by executing Python code that uses symbolic engines like SymPy and Z3 for math, logic, and code analysis.

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.

Word Live MCP Server

A Windows MCP server that attaches to a running Microsoft Word instance via COM automation and exposes 20 tools for reading, editing, formatting, and exporting documents — live on screen.

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.

Apache Druid MCP Server

Enables querying and managing Apache Druid datasources through natural language, including SQL queries, datasource exploration, and cluster connectivity testing.

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 Server

A testing environment for Model Context Protocol that enables exploration of MCP capabilities and integration of AI models with external data sources and tools.

mcp-edd

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