Azure MCP Server Integration

What does “Azure” MCP Server do?

The Azure MCP Server implements the Model Context Protocol (MCP) specification to create a seamless connection between AI agents and Azure services. It acts as a bridge, enabling AI assistants to interact with external data sources, APIs, and services provided by Azure. This integration enhances development workflows by allowing AI models to perform tasks such as database queries, file management, and API interactions—leveraging Azure’s vast cloud ecosystem. Designed for compatibility with tools like GitHub Copilot for Azure, the server enables developers to automate, orchestrate, and manage Azure resources directly from their AI-powered agents, streamlining complex development and operational scenarios.

List of Prompts

No information available in the repository about prompt templates.

List of Resources

No information available in the repository about specific resources exposed by the server.

List of Tools

No information available in the repository about tools provided by the server (e.g., from a server.py or similar file).

Use Cases of this MCP Server

• VS Code Automation: Enables AI agents (e.g., GitHub Copilot) to interact with Azure services directly from within VS Code, streamlining developer workflows.
• Azure Resource Management: Allows querying, creation, and management of Azure resources through AI-driven commands, reducing manual cloud operations.
• API Integration: Acts as a conduit for connecting AI agents to Azure APIs, facilitating the automation of cloud tasks such as deployments, scaling, and monitoring.
• Enhanced Developer Productivity: Integrates with tools such as the GitHub Copilot for Azure extension to facilitate rapid prototyping and debugging of cloud applications.
• Custom Workflow Orchestration: Supports building custom workflows that leverage both AI and Azure services for advanced automation scenarios.

How to set it up

Windsurf

1. Ensure Node.js 20 or later is installed.
2. Open your Windsurf configuration file.
3. Add the Azure MCP Server using the provided JSON snippet.
4. Save the configuration and restart Windsurf.
5. Verify the Azure MCP Server is active.

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

Securing API Keys Example:

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

Claude

1. Install Node.js 20+.
2. Locate the Claude integration or configuration file.
3. Add the Azure MCP Server definition.
4. Save and restart Claude.
5. Confirm the server is connected.

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

Cursor

1. Install the latest Node.js.
2. Open the Cursor configuration settings.
3. Insert the Azure MCP Server as shown below.
4. Save your changes and restart Cursor.
5. Check for server initialization messages.

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

Cline

1. Ensure Node.js 20 or higher is installed.
2. Access the Cline config file.
3. Register the Azure MCP Server using JSON.
4. Save and restart Cline.
5. Validate connectivity.

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

Note: Protect your API keys using environment variables as shown in the Windsurf example above.

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": {
    "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” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the available documentation and code, the Azure MCP Server provides a robust integration point for Azure and AI agents but lacks detailed public documentation on prompts, resources, and tools. Its setup is straightforward and secure, but the lack of granular technical detail limits its current evaluation. I would rate this MCP server a 6/10 for now; it covers essential integration and security but needs more transparency about its capabilities.

MCP Score