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Server Quality Checklist

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  • Latest release: v2.0.0

  • Disambiguation5/5

    The two tools target clearly distinct artifacts: a requirements definition document versus a functional specification document. The naming and descriptions make the boundary obvious, so an agent should not confuse them.

    Naming Consistency5/5

    Both tools follow the same generate_<target>_spec pattern using consistent snake_case. This creates a predictable and uniform naming convention across the entire server.

    Tool Count3/5

    With only two tools, the server feels thin and borderline for a document-generation server. Each tool is purposeful, but the set lacks supporting utilities or additional artifact types to feel fully scoped.

    Completeness3/5

    The pair covers the requirements-to-functional-spec handoff, but the workflow stops there. There is no way to update, version, or generate adjacent artifacts such as detailed design or test specifications, leaving notable gaps in a broader specification lifecycle.

  • Average 3.6/5 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
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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?

    With no annotations, the description carries the burden of explaining behavior. It states that the tool automatically generates a detailed requirements document with specific sections, which is useful. However, it does not disclose the output format, whether a file or text is returned, or any side effects.

    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, front-loaded sentence that immediately names the verb and resource, then adds the key inputs and output contents. There is no filler or redundant restatement of the tool name.

    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?

    The tool is straightforward and its parameters are fully described in the schema, but there is no output schema and the description does not state what kind of artifact or content the agent should expect in return. The missing relationship to generate_functional_spec also leaves a gap in an otherwise adequate description.

    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 schema already documents all five parameters. The description adds modest value by naming projectName, purpose, and background as generation inputs, but it does not clarify how constraints or targetUsers influence the output beyond their schema descriptions.

    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 a specific action and resource: generating a requirements definition document (要件定義書), and lists the inputs (project name, purpose, background) and output contents (functional requirements, non-functional requirements, constraints). It does not explicitly contrast with the sibling tool generate_functional_spec, but the resource name itself helps differentiate.

    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 gives no guidance on when to use this tool versus generate_functional_spec, and no exclusions or conditions. Since a closely related sibling tool exists, the lack of selection guidance leaves the agent to infer the boundary between requirement specs and functional specs.

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

  • Behavior3/5

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

    With no annotations, the description carries the behavioral disclosure burden. It does state that generation is automatic and enumerates output content (use cases, screen specs, data specs). However, it does not describe what the tool returns, whether it writes to a file, or any side effects, so some key behavior is left unspecified.

    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?

    Two short, purposeful Japanese sentences front-load the action and then add necessary detail about input and output scope. No filler or redundant content.

    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 absence of an output schema and annotations, the description should explain the return value or resulting artifact. It only says a specification is 'generated' without describing how the agent receives the result, and it ignores the optional requirementSpec input. This is a significant gap for selecting and invoking the tool correctly.

    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 schema already describes all parameters with 100% coverage, so the baseline applies. The description only mentions the 'feature list' and omits the optional requirementSpec parameter, adding little meaning beyond what the schema provides.

    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 clearly states the tool generates functional specification documents ('機能仕様書を生成します') and elaborates that it creates detailed specs including use cases, screen specs, and data specs from the feature list. This specific verb+resource plus the feature-list source distinguishes it from the sibling generate_requirement_spec.

    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?

    The description communicates clear usage context: pass a feature list ('機能一覧から') to obtain a detailed functional specification. It does not explicitly mention when not to use it or point to the sibling alternative, but the source/input requirement provides clear 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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