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DrSmile444

copilot-status-mcp

by DrSmile444
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.3.4

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion. The tool's purpose is clearly and uniquely defined.

    Naming Consistency5/5

    The tool name follows a standard verb_noun pattern, is descriptive, and is internally consistent despite being the only tool.

    Tool Count3/5

    A single tool feels thin for a server, but it could be justified if the entire purpose is status retrieval. However, it falls at the borderline of the typical 3-15 tool range.

    Completeness5/5

    The tool comprehensively covers the domain of Copilot status, including session window, weekly usage, monthly quota, and premium models. No obvious gaps exist for its stated purpose.

  • Average 3.8/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 24 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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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

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations are present, so the description must carry the transparency burden. It discloses that the tool reads quota and rate-limit status without side effects, but does not mention auth requirements, error behavior, or external API interactions. Adequate but not comprehensive.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, well-structured sentence that front-loads the core purpose and efficiently enumerates the returned metrics. Every phrase adds value with no redundancy.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a status-checking tool with no output schema, the description sufficiently explains what the agent will receive and the main parameters are documented in the schema. It could note the response format or potential errors, but the core context is complete.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The input schema provides full descriptions for both parameters (timeoutMs and includeLogin), achieving 100% coverage. The tool description adds no parameter-specific meaning, so the baseline score of 3 applies.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description uses the specific verb 'Retrieve' and clearly identifies the resource as GitHub Copilot quota and rate-limit status. It lists concrete data points returned, making the tool's purpose unmistakable even without sibling comparison.

    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?

    No explicit when-to-use or alternative guidance is provided, but the description implies the tool is for checking Copilot limits before or during usage. Since there are no sibling tools, the lack of alternatives is acceptable, but clearer use-case framing would strengthen this dimension.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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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.

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