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Server Quality Checklist

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  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is singular and clearly defined as generating QR codes from text.

    Naming Consistency5/5

    The single tool name 'generate_qr_code' follows a clear verb_noun pattern. 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 with the broad name 'qrcode generator'. While it covers basic generation, typical QR code use cases might include features like reading/decoding QR codes, generating with logos, or batch processing, which are missing.

    Completeness2/5

    The tool surface is severely incomplete for a QR code generator domain. It only provides generation from text, lacking common operations like decoding QR codes, generating from URLs or other data types, or handling error correction levels, which are standard in such tools.

  • Average 3.1/5 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
  • 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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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. While it mentions the tool returns an image with description, it doesn't cover important behavioral aspects like error conditions, performance characteristics, rate limits, or authentication requirements. The description is minimal and lacks behavioral context beyond basic functionality.

    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 well-structured with a clear purpose statement followed by organized parameter documentation. It's appropriately sized for a 5-parameter tool. The bilingual text (English/Chinese) adds slight redundancy but doesn't significantly impact conciseness. Every sentence serves a purpose.

    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 that there's an output schema (though not provided in the context), the description doesn't need to explain return values. However, for a tool with 5 parameters and no annotations, the description should provide more behavioral context and usage guidance. The parameter documentation is good, but overall completeness is only adequate with clear gaps in behavioral transparency.

    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?

    The description provides detailed parameter information in the 'Args' section, explaining each parameter's purpose and constraints (e.g., 'Size of each box in pixels (1-50)'). Since schema description coverage is 0%, this information is crucial and adds significant value beyond the bare schema. The only minor gap is that it doesn't explain parameter interactions or provide examples.

    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: 'Generate QR code from text and return as image with description.' It specifies the verb (generate), resource (QR code), and output format (image with description). However, since there are no sibling tools, it doesn't need to differentiate from alternatives, so it can't achieve a perfect 5.

    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 alternatives, prerequisites, or typical use cases. It simply states what the tool does without context about when it's appropriate. With no sibling tools, this is less critical, but still a gap in usage guidance.

    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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