发现优秀的 MCP 服务器

Jellyseerr MCP Server

Enables interaction with Jellyseerr media request systems through natural language. Supports searching for media, creating requests, checking request status, and managing your media library workflow.

ClickHouse Cloud API MCP Server

A Multi-Agent Conversation Protocol server that enables interaction with the ClickHouse Cloud API, providing programmatic access to ClickHouse Cloud services through natural language.

Redis MCP Server

官方 Redis MCP 服务器是一个自然语言接口，专为代理应用设计，旨在高效地管理和搜索 Redis 中的数据。

Memory MCP Worker

Provides cross-device access to a persistent knowledge graph via Cloudflare Workers, enabling memory storage and retrieval through both MCP protocol and REST API with full-text search capabilities.

EdgeOne Pages MCP Server

A self-hosted MCP server that enables AI assistants to deploy and manage static websites directly on EdgeOne Pages platform with KV storage support.

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.

Resemble AI Voice Generation MCP Server

使用模型上下文协议与 Claude 和 Cursor 集成，以使用 Resemble AI 的声音从文本生成语音音频。

CloudBase MCP

Bridges AI IDEs with Tencent CloudBase for seamless deployment, enabling users to go from AI-generated code to live applications with automatic cloud resource configuration, database setup, and intelligent debugging.

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-server-exa-search

Zed 扩展，用于 Exa 的 MCP 服务器

hyper-mcp

一个快速、安全的 MCP 服务器，它通过 WebAssembly 插件扩展其功能。

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.

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.

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.

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.

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.

Mcpmapserver

为了学习，谷歌地图的 MCP 服务器。 (For study, Google Maps MCP server.) A more natural translation, depending on the context, could be: * **用于学习的谷歌地图 MCP 服务器:** (Google Maps MCP server for learning) - This is a more direct and common way to phrase it. * **研究谷歌地图 MCP 服务器:** (Researching Google Maps MCP server) - If the intention is to study the server itself. "MCP" likely refers to "Map Control Protocol" or something similar within the Google Maps architecture.

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.

Puppeteer

MCP Puppeteer 服务器 HomeAssistant 插件

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.

Deepseek R1 MCP Server

通过人工智能驱动的命令，实现浏览器自动化和实时计算机视觉任务，提供零成本的数字导航和交互，从而增强网络体验。

Git MCP Server Knowledge Base

Git MCP 服务器的实现、配置和故障排除的综合知识库

mcp-youtube-transcripts

MCP 服务器用于获取 YouTube 字幕。 (MCP 服务器用于获取 YouTube 字幕。)

P-Link-MCP

Payment & Transaction Tools that allow AI agents to send, receive, and request payments

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.

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.

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.

Vertica MCP Server

A Model Context Protocol server that enables AI assistants to interact with Vertica databases through SQL queries, schema inspection, database documentation, and data export capabilities.