Azure MCP Hub MCP Server

What does “Azure MCP Hub” MCP Server do?

Azure MCP Hub is a central resource for developers to build, run, or reuse Model Context Protocol (MCP) servers on Azure, supporting multiple programming languages including C#, Python, Java, and JavaScript. It acts as a guide and aggregator, providing links and references to sample servers, tools, resources, and SDKs to accelerate the development of AI agents that can interact with real APIs. By leveraging MCP, developers can connect AI assistants seamlessly to external data sources, APIs, or services, enabling enhanced workflows such as database queries, file management, and integration with development and infrastructure tools. The hub also highlights plug-and-play MCP servers for instant access to common APIs, streamlining development and reducing the need for manual integration work.

List of Prompts

No specific prompt templates are mentioned or provided in the repository.

List of Resources

No explicit MCP resources (as defined by the MCP protocol: data/content endpoints for context) are listed or described in this repository.

List of Tools

No server.py or equivalent implementation with tool definitions is present in the repository. This repository primarily serves as a hub of links to other MCP servers and SDKs.

Use Cases of this MCP Server

• Discovering MCP Server Samples: Quickly find open-source MCP servers for popular data and development APIs (Redis, PostgreSQL, MySQL, MongoDB, Azure CLI, Kubernetes, GitHub, Azure DevOps).
• Accelerating MCP Server Development: Access SDKs and code samples in multiple languages to build custom MCP servers.
• Integrating MCP with AI Frameworks: Learn to plug MCP servers into AI agent SDKs and frameworks (Semantic Kernel, LangChain.js, Spring AI, OpenAI Agents).
• Plug-and-Play API Access: Use prebuilt MCP servers to expose real APIs to AI agents with minimal setup.
• Learning and Contributing: Access protocol documentation and contribute new servers/tools via pull requests.

How to set it up

Windsurf

1. Ensure prerequisites are installed (e.g., Node.js, Windsurf).
2. Open Windsurf’s configuration file (often windsurf.json or a similar settings file).
3. Add the Azure MCP Hub server using a JSON snippet in the mcpServers section.
4. Save the configuration and restart Windsurf.
5. Verify the server is listed and accessible.

{
  "mcpServers": {
    "azure-mcp-hub": {
      "command": "npx",
      "args": ["@azure/mcp-server@latest"]
    }
  }
}

Securing API keys:

{
  "mcpServers": {
    "azure-mcp-hub": {
      "command": "npx",
      "args": ["@azure/mcp-server@latest"],
      "env": {
        "API_KEY": "${API_KEY}"
      },
      "inputs": {
        "apiKey": "${API_KEY}"
      }
    }
  }
}

Claude

1. Install Node.js and Claude (if required).
2. Locate Claude’s configuration file.
3. Add the Azure MCP Hub server block as shown.
4. Save changes and restart Claude.
5. Confirm successful integration.

{
  "mcpServers": {
    "azure-mcp-hub": {
      "command": "npx",
      "args": ["@azure/mcp-server@latest"]
    }
  }
}

Securing API keys:

{
  "mcpServers": {
    "azure-mcp-hub": {
      "command": "npx",
      "args": ["@azure/mcp-server@latest"],
      "env": {
        "API_KEY": "${API_KEY}"
      },
      "inputs": {
        "apiKey": "${API_KEY}"
      }
    }
  }
}

Cursor

1. Install prerequisites (Node.js, Cursor).
2. Edit Cursor’s MCP server configuration.
3. Insert the Azure MCP Hub server entry.
4. Save and restart Cursor.
5. Validate that the server is recognized.

{
  "mcpServers": {
    "azure-mcp-hub": {
      "command": "npx",
      "args": ["@azure/mcp-server@latest"]
    }
  }
}

Securing API keys:

{
  "mcpServers": {
    "azure-mcp-hub": {
      "command": "npx",
      "args": ["@azure/mcp-server@latest"],
      "env": {
        "API_KEY": "${API_KEY}"
      },
      "inputs": {
        "apiKey": "${API_KEY}"
      }
    }
  }
}

Cline

1. Make sure Node.js and Cline are set up.
2. Open Cline’s configuration file.
3. Add the Azure MCP Hub server as shown below.
4. Save and restart Cline.
5. Check for proper connection.

{
  "mcpServers": {
    "azure-mcp-hub": {
      "command": "npx",
      "args": ["@azure/mcp-server@latest"]
    }
  }
}

Securing API keys:

{
  "mcpServers": {
    "azure-mcp-hub": {
      "command": "npx",
      "args": ["@azure/mcp-server@latest"],
      "env": {
        "API_KEY": "${API_KEY}"
      },
      "inputs": {
        "apiKey": "${API_KEY}"
      }
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "azure-mcp-hub": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “azure-mcp-hub” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion:
This MCP hub repository is highly valuable as a reference and discovery resource but does not itself implement an MCP server with prompts, tools, or resources. It is best suited for developers seeking to explore or build MCP servers with guidance and links to working examples.

MCP Score

Rating:
Based on the above tables, this repository scores a 3/10 as an MCP server implementation (since it is a hub, not a server itself), but a 9/10 as a valuable reference and community resource for MCP development.