Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Azure Impact Reporting MCP Server Report connectivity issues with my VM named 'web-server' in resource group 'production-rg'"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.
Related MCP server: Azure Omni-Tool MCP Server
The impact-reporter.py script provides a Model Context Protocol server that:
Exposes a tool to report resource impacts to Azure
Automatically authenticates with Azure using DefaultAzureCredential
Creates workload impact reports via the Azure Management API
Handles parameter extraction from natural language requests
Can ask for additional details if the request is missing required information
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
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
git clone https://github.com/yourusername/MCP-Server-Azure-Impact-Reporting.git
cd MCP-Server-Azure-Impact-Reportingpip install -r requirements.txtOr install them manually:
pip install mcp[cli] azure-identity httpxThe 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"
]
}uv CommandThe 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
If you prefer not to use uv, you can run the MCP server directly:
Create and activate a virtual environment:
python -m venv .venv
# On Windows
.venv\Scripts\activate
# On macOS/Linux
source .venv/bin/activateInstall dependencies:
pip install -r requirements.txtRun the server directly:
python impact-reporter.pyIf 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:
"Report connectivity issues with my VM named 'web-server' in resource group 'production-rg'"
"Let Azure know my SQL database 'customer-db' in 'data-rg' is experiencing performance issues"
"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:
When additional information is required
Request for additional details
Infer the details and report impact
The impact reporting tool uses the Azure Management API (2023-12-01-preview) to create workload impact reports.
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
Reports the impact to Azure. Typically called when customers facing issue with azure infrastructure and they want to let azure know about it.
Args: subscriptionid (str): The Azure subscription ID where the resource is present. Eg: 68fa15fd-eef2-4ca3-a053-bcf268bd7371 resourcegroup (str): The Azure resource group name where the resource is present. Eg: test-rg resourceprovider (str): The Azure resource provider name for the resource. Eg: Microsoft.Compute resourcetype (str): The Azure resource type. Eg: virtualMachines resourcename (str): The Azure resource name. Eg: test-vm impactcategory (str): The impact category denoting the underlying issue. Can be one of: Resource.Connectivity, Resource.Performance, Resource.Availability or Resource.Unknown if the issue is not known.
| Name | Required | Description | Default |
|---|---|---|---|
| subscriptionid | Yes | ||
| resourcegroup | Yes | ||
| resourceprovider | Yes | ||
| resourcetype | Yes | ||
| resourcename | Yes | ||
| impactcategory | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the action 'reports' but does not clarify whether this is a read-only operation, if it requires specific permissions, what the response looks like, or any side effects like notifications or logging. For a tool with no annotation coverage, this leaves significant behavioral gaps, though it at least hints at a reporting function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, starting with the tool's purpose and typical usage, followed by a structured 'Args:' section. Each sentence adds value, with no redundant information. It could be slightly more concise by integrating the usage context more seamlessly, but overall it's efficient and well-organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (6 parameters, no annotations, no output schema), the description is partially complete. It covers parameter semantics well but lacks details on behavioral aspects like response format, error handling, or authentication needs. Without an output schema, it should ideally explain what the tool returns, but it doesn't. It's adequate for basic use but has notable gaps for a mutation-like reporting tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds meaning by explaining each parameter's purpose with examples (e.g., subscriptionid as 'The Azure subscription ID where the resource is present') and provides allowed values for impactcategory ('Resource.Connectivity, Resource.Performance, Resource.Availability or Resource.Unknown'). This effectively documents all 6 parameters, though it could be more detailed on resourceprovider and resourcetype.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Reports the impact to Azure' with the context 'when customers facing issue with azure infrastructure and they want to let azure know about it.' This specifies the verb ('reports'), resource ('Azure'), and typical usage scenario. However, without sibling tools, it cannot demonstrate differentiation from alternatives, preventing a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides implied usage guidelines by stating 'Typically called when customers facing issue with azure infrastructure and they want to let azure know about it.' This gives context for when to use the tool but lacks explicit guidance on when not to use it or alternatives, as there are no sibling tools mentioned. It's adequate but has clear gaps in specificity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
report_impact_to_azureWith only one tool, there is no possibility of confusion or overlap between tools. The single tool has a clear, distinct purpose: reporting impact to Azure for infrastructure issues.
The single tool name follows a clear verb_noun pattern (report_impact_to_azure). Since there is only one tool, consistency is inherently perfect with no deviations to assess.
One tool is too few for a server named 'Azure Impact Reporting MCP Server', which suggests a broader scope for impact reporting in Azure. A single tool feels thin and incomplete for handling various aspects of impact reporting, such as querying, updating, or managing reports.
The tool surface is severely incomplete for impact reporting. It only allows reporting impact but lacks essential operations like retrieving existing reports, updating reports, deleting reports, or listing reports, which are necessary for a full lifecycle of impact management in Azure.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Provides capabilities that let LLM agents perform a range of infrastructure management tasks.
Enable Large Language Model clients to interact seamlessly with any MediaWiki wiki. Perform action…
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