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Server Quality Checklist

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  • Latest release: v1.1.1

  • Disambiguation5/5

    Each tool has a distinct purpose: create generates a set, get retrieves a single rendered image, and regenerate re-renders with modifications. There is no overlap in their actions.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (create_illustrations, get_illustration, regenerate_illustration), with clear and uniform structure.

    Tool Count5/5

    Three tools is well-scoped for a focused illustration generation server, covering creation, retrieval, and regeneration without unnecessary bloat.

    Completeness4/5

    The core workflow (create, get, regenerate) is fully covered. A delete or list-all tool is missing, but for the intended use case of generating and viewing a set, the surface is complete enough.

  • Average 3.8/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 66 commits 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

  • Behavior2/5

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

    With no annotations, the description must carry behavioral disclosure. It mentions non-determinism and returning the new image in `_meta`, but fails to disclose side effects like replacing the cached image (when setId is used), permission needs, or rate limits. This is a significant gap for a mutation-like tool.

    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 two sentences, immediately states the primary action, and includes a concise rationale for regenerating. There is no redundancy or filler.

    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 tool's complexity (9 parameters, no output schema, no annotations), the description is incomplete. It leaves critical operational details unaddressed—how to select the illustration, whether the original is replaced, and the relationship between index and setId—making it insufficient for correct agent invocation.

    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 description coverage is only 56%, and the description only adds meaning for 'prompt' and 'aspect' by naming them as tweakable. It does not explain how to target a specific illustration via index/setId, nor clarify parameters like title, archetype, resolution. Description fails to compensate for the schema's gaps.

    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 function: 'Re-render a single illustration with the same (or a tweaked) prompt and aspect.' This specific verb and resource distinguish it from siblings like create_illustrations (creating new) and get_illustration (retrieving).

    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 for obtaining a fresh take ('Image models are non-deterministic, so this yields a fresh take') but does not explicitly contrast with create_illustrations or get_illustration, nor provides when-not-to-use conditions. The guidance is implied, not explicit.

    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?

    No annotations are provided, so the description carries the burden. It discloses that the server applies the Tinku character, conceptual engine, and house style automatically, and that an interactive viewer opens. However, it does not mention potential side effects (e.g., file creation, cost, wait time) or any rate limits, leaving some behavioral gaps.

    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 three sentences, front-loaded with the core purpose, followed by prompt composition guidance and usage context. Every sentence adds value with no redundancy or filler.

    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 (multiple illustrations, prompt rules, style references), the description covers the essential aspects: output format (16:9, optional 21:9), viewer behavior, and server-side defaults. It does not describe return values, but the viewer aspect covers the output experience, and no output schema exists. Sibling tools are not explicitly differentiated, but the description is reasonably complete for a create operation.

    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?

    Schema coverage is 100% so a baseline of 3 applies. The description adds meaningful context for the prompt parameter, explaining what it should contain and that the server handles character/style automatically. This goes beyond the schema's field descriptions, justifying a 4.

    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 renders a set of hand-drawn Tinku illustrations and opens an interactive viewer. It specifies the resource and action (render + open viewer), and distinguishes from siblings by focusing on creation of a set, though it does not explicitly name alternative tools.

    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?

    Explicitly states when to use the tool: 'Use this when the user wants hand-drawn illustrations for an article, post, or explainer.' It also provides guidance on supplying prompts. It lacks explicit exclusions or when to prefer get_illustration/regenerate_illustration, but the context is clear.

    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, the description carries the full burden. It discloses that the tool returns one image at a time and requires a previously created set, which is useful behavioral context. However, it does not mention potential errors, image format, or side effects (which are likely none), so it is not fully comprehensive but adequate for a simple read operation.

    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 two sentences long, front-loaded with the core purpose, and every word earns its place. It conveys purpose, usage context, and a key behavioral trait without 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?

    Given no output schema, the description explains the return value ('rendered image') and the intended workflow ('after create_illustrations', 'one at a time'). It is complete for a straightforward fetch tool, but could add details like image format or error handling for exceptional cases. Overall, it provides sufficient context for an agent to use it 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?

    Schema description coverage is 100%, with both parameters (setId and index) clearly described in the schema. The tool description adds minimal new meaning beyond reinforcing that the set was created by create_illustrations and that index refers to an illustration within that set. Baseline 3 applies because the schema already documents the parameters sufficiently.

    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 returns a rendered image for one illustration from a previously created set. It uses a specific verb ('Return') and resource ('rendered image for one illustration'), and distinguishes itself from siblings create_illustrations and regenerate_illustration by focusing on fetching a single item rather than creating or regenerating.

    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 gives explicit usage context: the viewer calls this after create_illustrations, and it is used per illustration so that a single tool result never carries the whole set. This implies the correct workflow and differentiates from batch operations, though it does not explicitly mention when not to use it or compare to regenerate_illustration.

    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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