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Server Quality Checklist

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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one generates a pedigree tree in visual formats, while the other provides documentation about the data format. There is no overlap in functionality, making it impossible for an agent to confuse them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern (generate_pedigree, get_pedigree_documentation) with clear, descriptive names that align well with their functions. There are no deviations or mixed conventions.

    Tool Count2/5

    With only two tools, the server feels under-scoped for a pedigree domain, which typically involves more operations like data validation, editing, or querying. While the tools cover core tasks, the count is too low for comprehensive coverage.

    Completeness2/5

    The server lacks essential operations for a pedigree system, such as creating, updating, or deleting pedigree data, validating input, or querying specific individuals. The tools only handle generation and documentation, leaving significant gaps that will likely cause agent failures in real-world workflows.

  • Average 4.5/5 across 2 of 2 tools scored.

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

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

  • Behavior4/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: it generates visual output (PNG/SVG), follows a specific standard (Bennett 2008), and includes important constraints (e.g., siblings share parents, top_level usage rules). However, it lacks details on error handling, performance limits, or authentication needs, which would be beneficial for a tool with complex input.

    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 highly concise and well-structured: two sentences that front-load the core purpose and follow with critical usage rules. Every sentence earns its place by providing essential information without redundancy, making it efficient and easy to parse.

    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?

    Given the tool's complexity (7 parameters, nested dataset structure) and lack of annotations or output schema, the description is largely complete. It covers the tool's purpose, key behavioral rules, and output formats. However, it does not describe the return value (e.g., base64 string for PNG, XML for SVG) or potential errors, which would enhance completeness for a generative tool.

    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 parameters thoroughly. The description adds minimal parameter semantics beyond the schema, primarily emphasizing rules for 'mother/father' and 'top_level' usage. It does not explain parameter interactions or provide additional context for other parameters, resulting in a baseline score of 3.

    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's purpose: 'Generates a pedigree tree (Bennett 2008 standard) in PNG or SVG format.' It specifies the verb ('generates'), resource ('pedigree tree'), standard ('Bennett 2008'), and output formats ('PNG or SVG'). This distinguishes it from the sibling tool 'get_pedigree_documentation', which likely provides documentation rather than generating visualizations.

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

    Usage Guidelines5/5

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

    The description provides explicit usage guidelines: 'IMPORTANT: Use mother/father for ALL individuals with known parents - siblings share same parents. Only use top_level:true for founders with NO known parents.' It specifies when to use certain parameters (mother/father vs. top_level) and includes critical constraints, offering clear guidance on how to structure the dataset correctly.

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

  • Behavior4/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly indicates this is a read-only operation ('Returns documentation') and specifies the scope ('comprehensive documentation for the pedigree data format'). However, it doesn't mention potential limitations like response format, size constraints, or error conditions.

    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 perfectly concise with two sentences that each serve distinct purposes: the first states what the tool does, the second provides critical usage guidance. There is zero wasted language or 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 0-parameter tool with no output schema, the description provides excellent context about what information will be returned and when to use it. The only minor gap is the lack of information about the format or structure of the returned documentation.

    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 tool has 0 parameters with 100% schema description coverage, so the baseline would be 4. The description appropriately doesn't discuss parameters since there are none, and instead focuses on the tool's purpose and usage context.

    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's purpose with specific verb ('Returns') and resource ('comprehensive documentation for the pedigree data format'). It explicitly distinguishes from its sibling tool 'generate_pedigree' by stating this should be called first before generating a pedigree.

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

    Usage Guidelines5/5

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

    The description provides explicit usage guidance: 'ALWAYS call this first before generating a pedigree.' It clearly positions this as a prerequisite to the sibling tool 'generate_pedigree' and specifies the context in which it should be used.

    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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  • Evaluate tool definition quality.

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