Skip to main content
Glama
chand45

Azure Impact Reporting MCP Server

by chand45

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    With 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.

    Naming Consistency5/5

    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.

    Tool Count2/5

    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.

    Completeness2/5

    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.

  • Average 3.3/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available
  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
      "$schema": "https://glama.ai/mcp/schemas/server.json",
      "maintainers": [
        "your-github-username"
      ]
    }

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

    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.

    Conciseness4/5

    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.

    Completeness3/5

    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.

    Parameters4/5

    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.

    Purpose4/5

    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.

    Usage Guidelines3/5

    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.

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

MCP-Server-Azure-Impact-Reporting MCP server

Copy to your README.md:

Score Badge

MCP-Server-Azure-Impact-Reporting MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/chand45/MCP-Server-Azure-Impact-Reporting'

If you have feedback or need assistance with the MCP directory API, please join our Discord server