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JigeeshaJain

gh-review-queue-mcp

Server Quality Checklist

92%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    The set contains only one tool, so there is no possibility of overlap or selecting the wrong tool. Its purpose is clearly and specifically described.

    Naming Consistency5/5

    The single tool name follows the conventional verb_noun pattern with a clear action and resource. There are no other tool names to create inconsistency.

    Tool Count4/5

    One tool is small, but the server is narrow by design: it exists specifically to fetch a GitHub review queue. The tool is substantial rather than trivial, so the count is slightly lean but still appropriate for the server's scope.

    Completeness5/5

    The tool covers the full review queue surface described: own PRs, requested changes, direct review requests, and team review requests, along with ranking reasons and match counts. There are no obvious read-model gaps within this narrow domain.

  • Average 4.3/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 7 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
  • This repository is licensed under Apache 2.0.

  • This repository includes a README.md file.

  • Tools from this server were used 2 times in the last 30 days.

  • This repository includes a glama.json configuration file.

  • This server has been verified by its author.

  • Add related servers to improve discoverability.

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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

  • Behavior4/5

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

    With no annotations provided, the description carries the burden of disclosure. It reveals the ranking tiers, tie-breaking rules, and the fact that results include priority_reasons and total_matching. It does not discuss auth, errors, or side effects, but the operation is clearly read-oriented and described in useful detail.

    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 front-loaded with the core purpose and ranking intent, then economically conveys the tier order and output signals in two structurally clear runs. Every clause earns its place and no filler exists.

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

    Completeness5/5

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

    The description is complete enough for reliable invocation. It covers behavior, output information, ordering, and scoping semantics, the output schema and full parameter documentation handle the remaining return-value details, and there are no required parameters or sibling tools to complicate selection.

    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?

    Schema description coverage is 100%, so the baseline is 3. The description does not elaborate on the individual parameters such as limit, repos, include, max_age_days, or exclude_drafts, but it does not need to because those parameters are already well-documented in the input schema.

    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 states a specific verb and resource: "Return the viewer's GitHub pull request review queue," and goes further by specifying the exact ranking logic. It is immediately clear what this tool does and how it differs from a generic list-pull-requests tool.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    There are no siblings to contrast against, so the explicit when/when-not language is less necessary. The description makes the intended use clear: retrieve a prioritized review queue with tiered attention ordering, which is sufficient context for an agent to select it.

    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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  • 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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