Azure Impact Reporter

by chand45

2 stars

238 downloads

Not rated

GitHub

About

Enables AI to report Azure infrastructure issues by authenticating with Azure credentials and submitting standardized workload impact reports through the Azure Management API

Details

Author: chand45
Repository: chand45/MCP-Server-Azure-Impact-Reporting
GitHub stars: 2
Downloads: 238
Categories: Design, AI, API, Frontend, Cloud Service, Infrastructure

- Exposes a tool for LLMs to report resource impacts to Azure
- Automatic authentication via DefaultAzureCredential
- Creates workload impact reports through the Azure Management API
- Extracts required parameters from natural language requests
- Asks for additional details when information is missing
- Supports multiple impact categories (Connectivity, Performance, Availability, Unknown)

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below
Plugin Name Azure Impact Reporter

Command (node, npx, python, etc.) uv
Arguments
- Argument 1 --directory
- Argument 2 ABSOLUTE_PATH_TO_ROOT_FOLDER
- Argument 3 run
- Argument 4 impact-reporter.py
Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "azure impact reporter": {
            "cwd": "ABSOLUTE_PATH_TO_ROOT_FOLDER",
            "env": {},
            "args": [
                "--directory",
                "ABSOLUTE_PATH_TO_ROOT_FOLDER",
                "run",
                "impact-reporter.py"
            ],
            "command": "uv"
        }
    }
}

JSON

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

JSON

{
    "mcpServers": {
        "azure impact reporter": {
            "cwd": "ABSOLUTE_PATH_TO_ROOT_FOLDER",
            "env": {},
            "args": [
                "--directory",
                "ABSOLUTE_PATH_TO_ROOT_FOLDER",
                "run",
                "impact-reporter.py"
            ],
            "command": "uv"
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "azure impact reporter": {
            "cwd": "ABSOLUTE_PATH_TO_ROOT_FOLDER",
            "env": {},
            "args": [
                "--directory",
                "ABSOLUTE_PATH_TO_ROOT_FOLDER",
                "run",
                "impact-reporter.py"
            ],
            "command": "uv"
        }
    }
}

JSON

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

JSON

{
    "mcpServers": {
        "azure impact reporter": {
            "cwd": "ABSOLUTE_PATH_TO_ROOT_FOLDER",
            "env": {},
            "args": [
                "--directory",
                "ABSOLUTE_PATH_TO_ROOT_FOLDER",
                "run",
                "impact-reporter.py"
            ],
            "command": "uv"
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "azure impact reporter": {
            "cwd": "ABSOLUTE_PATH_TO_ROOT_FOLDER",
            "env": {},
            "args": [
                "--directory",
                "ABSOLUTE_PATH_TO_ROOT_FOLDER",
                "run",
                "impact-reporter.py"
            ],
            "command": "uv"
        }
    }
}

JSON

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

JSON

{
    "mcpServers": {
        "azure impact reporter": {
            "cwd": "ABSOLUTE_PATH_TO_ROOT_FOLDER",
            "env": {},
            "args": [
                "--directory",
                "ABSOLUTE_PATH_TO_ROOT_FOLDER",
                "run",
                "impact-reporter.py"
            ],
            "command": "uv"
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "azure impact reporter": {
            "cwd": "ABSOLUTE_PATH_TO_ROOT_FOLDER",
            "env": {},
            "args": [
                "--directory",
                "ABSOLUTE_PATH_TO_ROOT_FOLDER",
                "run",
                "impact-reporter.py"
            ],
            "command": "uv"
        }
    }
}

JSON

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

JSON

{
    "mcpServers": {
        "azure impact reporter": {
            "cwd": "ABSOLUTE_PATH_TO_ROOT_FOLDER",
            "env": {},
            "args": [
                "--directory",
                "ABSOLUTE_PATH_TO_ROOT_FOLDER",
                "run",
                "impact-reporter.py"
            ],
            "command": "uv"
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "azure impact reporter": {
            "cwd": "ABSOLUTE_PATH_TO_ROOT_FOLDER",
            "env": {},
            "args": [
                "--directory",
                "ABSOLUTE_PATH_TO_ROOT_FOLDER",
                "run",
                "impact-reporter.py"
            ],
            "command": "uv"
        }
    }
}

JSON

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

JSON

{
    "mcpServers": {
        "azure impact reporter": {
            "cwd": "ABSOLUTE_PATH_TO_ROOT_FOLDER",
            "env": {},
            "args": [
                "--directory",
                "ABSOLUTE_PATH_TO_ROOT_FOLDER",
                "run",
                "impact-reporter.py"
            ],
            "command": "uv"
        }
    }
}

From the repository

pip install -r requirements.txt

Or install them manually:

pip install mcp[cli] azure-identity httpx

The tool uses DefaultAzureCredential for authentication. Ensure you're logged in to Azure with 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

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 where you cloned this repository.

For example:

"impactreporter": {

    "command": "uv",

    "args": [

        "--directory",

        "C:\\Users\\username\\source\\repos\\MCP-Server-Azure-Impact-Reporting",

        "run",

        "impact-reporter.py"

    ]

}

If you're using Claude with Desktop or another MCP-enabled client, the server will start automatically when needed.

Once configured, your LLM can report impacts with natural language requests like:

1. "Report connectivity issues with my VM named 'web-server' in resource group 'production-rg'"
2. "Let Azure know my SQL database 'customer-db' in 'data-rg' is experiencing performance issues"
3. "Report that my App Service 'api-service' is down"

The MCP server will automatically parse these requests and ask for any missing parameters before submitting the report to Azure.

Example Converstations:
alt text

When additional information is required
1. Request for additional details
alt text

2. Infer the details and report impact
alt text

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "azure impact reporter": {
            "cwd": "ABSOLUTE_PATH_TO_ROOT_FOLDER",
            "env": {},
            "args": [
                "--directory",
                "ABSOLUTE_PATH_TO_ROOT_FOLDER",
                "run",
                "impact-reporter.py"
            ],
            "command": "uv"
        }
    }
}

Linux

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

Macos

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

Windows

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

MCP-Server-Azure-Impact-Reporting

Overview

The 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 are facing issues with Azure infrastructure.

Functionality

The impact-reporter.py script provides a Model Context Protocol server that:

1. Exposes a tool to report resource impacts to Azure
2. Automatically authenticates with Azure using DefaultAzureCredential
3. Creates workload impact reports via the Azure Management API
4. Handles parameter extraction from natural language requests
5. Can ask for additional details if the request is missing required information

Impact Categories

The tool supports the following impact categories: - Resource.Connectivity - For connectivity issues with Azure resources - Resource.Performance - For performance degradation issues - Resource.Availability - For availability or downtime issues - Resource.Unknown - When the specific issue type is not known

Requirements

- Python 3.8+ - mcp[cli] - Model Context Protocol package with CLI support - azure-identity - For Azure authentication - httpx - For making HTTP requests to 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 them manually:

pip install mcp[cli] azure-identity httpx

3. Azure Authentication Setup

The tool uses DefaultAzureCredential for authentication. Ensure you're logged in to Azure with 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 your 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 where you cloned this repository.

For example:

"impactreporter": {

    "command": "uv",

    "args": [

        "--directory",

        "C:\\Users\\username\\source\\repos\\MCP-Server-Azure-Impact-Reporting",

        "run",

        "impact-reporter.py"

    ]

}

Understanding the `uv` Command

The uv command in the configuration uses pyproject.toml to manage dependencies:

- Virtual Environment: uv creates and manages its own internal virtual environment separate from any .venv you may have created
- Dependency Management: Dependencies are automatically installed based on pyproject.toml specifications
- Isolation: The uv cache system ensures no interference with your local Python environment

Alternative: Direct Python Execution

If you prefer not to use uv, you can run the MCP server directly:

1. Create and activate a virtual environment:

    python -m venv .venv

    # On Windows

    .venv\Scripts\activate

    # On macOS/Linux

    source .venv/bin/activate

2. Install dependencies:

    pip install -r requirements.txt

3. Run the server directly:

    python impact-reporter.py

5. Running the MCP Server

If you're using Claude with Desktop or another MCP-enabled client, the server will start automatically when needed.

Usage Examples

Once configured, your LLM can report impacts with natural language requests like:

The MCP server will automatically parse these requests and ask for any missing parameters before submitting the report to Azure.

Example Converstations:
alt text

When additional information is required
1. Request for additional details
alt text

2. Infer the details and report impact
alt text

API Details

The impact reporting tool uses the Azure Management API (2023-12-01-preview) to create workload impact reports.

Troubleshooting

- Authentication issues: Ensure you're logged into Azure and have proper permissions - Missing parameters: The tool will ask for additional details if needed - API errors: Check Azure portal to ensure your subscription and resources exist

License

MIT License

No reviews yet — be the first

Use Google, GitHub, or an email account so ratings stay tied to real people.

Email sign in

No reviews posted yet.

About

Details

Jump to

Setting up with Highlight

From the repository

Claude Desktop / Cursor

Linux

Macos

Windows

MCP-Server-Azure-Impact-Reporting

Overview

Functionality

Impact Categories

Requirements

Setup Instructions

1. Clone the repository

2. Install dependencies

3. Azure Authentication Setup

4. Configure your MCP client

Understanding the uv Command

Alternative: Direct Python Execution

5. Running the MCP Server

Usage Examples

API Details

Troubleshooting

License

Install

More like this

GitLab

Context 7

1Panel

🔎 GPT Researcher

Serena

FastMCP v2 🚀

Understanding the `uv` Command