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QinyangTan

github-readme-mcp

by QinyangTan
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Server Quality Checklist

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  • Latest release: v1.0.0

  • Disambiguation3/5

    The two tools have distinct inputs and outputs (compare vs. suggest), but the reference to a non-existent 'analyze_readme' tool in the suggest tool's description creates ambiguity about the intended workflow.

    Naming Consistency4/5

    Both tools follow a consistent verb_noun pattern with snake_case, but the missing 'analyze_readme' tool referenced in the description is a minor inconsistency.

    Tool Count2/5

    With only 2 tools, the server feels incomplete for its stated purpose of README analysis. A typical set would include at least 3-5 tools for a well-scoped domain.

    Completeness2/5

    The server lacks a basic 'analyze_readme' tool that is referenced in the suggest tool's description, and the surface only covers comparison and suggestion, missing standalone analysis and likely other operations.

  • Average 2.9/5 across 2 of 2 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

  • This repository is licensed under MIT License.

  • 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.

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  "$schema": "https://glama.ai/mcp/schemas/server.json",
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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

  • 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. It does not disclose whether the tool is read-only, requires authentication, or has any side effects. The description only covers the output format, leaving behavioral traits opaque.

    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 short and to the point, but the phrase 'Same inputs as analyze_readme' adds a dependency on another tool's description, reducing self-containment. Still, every sentence adds value.

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

    Completeness2/5

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

    Given the complexity of 4 parameters with no schema descriptions and no output schema, the description is inadequate. It does not explain the return format in detail, and the reliance on analyze_readme for inputs is a gap. The tool needs more information to be used effectively.

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

    Parameters1/5

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

    Schema description coverage is 0%, and the description only says 'Same inputs as analyze_readme' without describing any of the four parameters. This is insufficient for an agent to understand what values to provide, especially since analyze_readme is not described here.

    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 it returns grouped recommendations with rationale and example text, and it mentions the inputs are the same as analyze_readme. However, it does not explicitly state what the tool does in terms of its core function (i.e., improving READMEs) beyond the output structure.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus the sibling tool compare_before_after. It references analyze_readme for inputs but does not explain how or when this tool should be preferred, and analyze_readme is not listed as a sibling, which could cause confusion.

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

  • Behavior2/5

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

    No annotations are provided, so the description must convey behavioral traits. It only says 'compare and summarize', with no mention of side effects, authorization needs, rate limits, or any limitations. This is minimal behavioral disclosure.

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

    Conciseness3/5

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

    The description is a single sentence with no fluff, but it omits important details. It is concise but not adequately informative.

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

    Completeness2/5

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

    Given no annotations, no output schema, and a comparison tool, the description should explain the output format or criteria for improvement/gaps. It only vaguely mentions 'summarize improvements vs remaining gaps', leaving agents to guess the output structure. The sibling tool further calls for differentiation which is missing.

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

    Parameters2/5

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

    Schema coverage is 0% (no parameter descriptions). The description adds the phrase 'two README markdown strings', which maps to the parameters 'original' and 'improved', but does not provide expected format, constraints, or examples. This is insufficient compensation for the missing schema descriptions.

    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 explicitly states the verb 'compare', the resource 'two README markdown strings', and the outcome 'summarize improvements vs remaining gaps'. It clearly distinguishes from sibling tool 'suggest_readme_improvements' which likely focuses on suggesting improvements rather than comparing two versions.

    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 implies usage when an original and improved version are available, but provides no explicit guidance on when to use this tool versus the sibling 'suggest_readme_improvements', nor any exclusions or prerequisites.

    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 there are no obvious security issues.
  • Evaluate tool definition quality.

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