MCP Server Azure Impact Reporting

🚀 Azure Impact Reporting MCP Server

This Azure Impact Reporting MCP (Model Context Protocol) Server enables Large Language Models (LLMs) to report impacts to Azure resources. This tool allows LLMs to automatically parse user requests, understand the required parameters, and submit reports to Azure when customers encounter issues with Azure infrastructure.

✨ Features

The impact-reporter.py script provides an MCP server that:

1. Offers a tool to report resource impacts to Azure.
2. Automatically authenticates to Azure using DefaultAzureCredential.
3. Creates workload impact reports via the Azure Management API.
4. Handles parameter extraction from natural language requests.
5. Can request more details when a request lacks required information.

Impact Categories

The tool supports the following impact categories:

• Resource.Connectivity - For Azure resource connectivity issues.
• Resource.Performance - For performance degradation issues.
• Resource.Availability - For availability or downtime issues.
• Resource.Unknown - When the specific issue type is unknown.

📦 Installation

Prerequisites

• Python 3.8+
• mcp[cli] - Model Context Protocol package with CLI support.
• azure-identity - For Azure authentication.
• httpx - For sending HTTP requests to the Azure API.

Setup Instructions

1. Clone the Repository

git clone https://github.com/yourusername/MCP-Server-Azure-Impact-Reporting.git
cd MCP-Server-Azure-Impact-Reporting

2. Install Dependencies

pip install -r requirements.txt

Or install manually:

pip install mcp[cli] azure-identity httpx

3. Azure Authentication Setup

The tool uses DefaultAzureCredential for authentication. Ensure you are logged in to Azure using one of the following methods:

• Azure CLI (az login).
• Visual Studio Code Azure Account extension.
• Azure PowerShell (Connect-AzAccount).
• Environment variables for service principal authentication.

4. Configure the MCP Client

Add the following configuration to your MCP client configuration file (e.g., claude_desktop_config.json):

"impactreporter": {
    "command": "uv",
    "args": [
        "--directory",
        "ABSOLUTE_PATH_TO_ROOT_FOLDER",
        "run",
        "impact-reporter.py"
    ]
}

Replace ABSOLUTE_PATH_TO_ROOT_FOLDER with the absolute path to the root folder of your cloned repository.

For example:

"impactreporter": {
    "command": "uv",
    "args": [
        "--directory",
        "C:\\Users\\username\\source\\repos\\MCP-Server-Azure-Impact-Reporting",
        "run",
        "impact-reporter.py"
    ]
}

5. Run the MCP Server

If you are using Claude Desktop or other MCP - supported clients, the server will start automatically when needed.

💻 Usage Examples

Basic Usage

After configuration, your LLM can report impacts through natural language requests, such as:

1. "Report connectivity issues for the VM named 'web - server' in the resource group 'production - rg'."
2. "Let Azure know that my SQL database 'customer - db' in 'data - rg' has performance issues."
3. "Report that my 'app - service' is down."

The MCP server will automatically parse these requests, request more details when necessary, and then submit the reports to Azure.

Example conversation:

Advanced Usage

When more details are needed:

1. Request additional information
2. Continue after providing more information

🔧 Technical Details

• Supported resource types: Virtual machines, databases, storage accounts, etc.
• Impact reports include: Performance metrics, error logs, resource status, etc.
• Automatic generation of repair suggestions: Provide solutions based on the collected data.

📚 Image Descriptions

• ReportImpact.png: Displays the impact report generation process.
• RequestDetails.png: Displays the interface for requesting additional details.