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Server Quality Checklist

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  • Latest release: v0.1.7

  • Disambiguation5/5

    Each tool targets a distinct image operation: generating from scratch, editing, composing multiple images, and style transfer. No overlap in functionality.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with snake_case (e.g., compose_images, edit_image). No deviations.

    Tool Count5/5

    Four tools cover essential image generation capabilities without being sparse or overloaded. The scope is well-balanced.

    Completeness4/5

    Core image workflows (generation, editing, composition, style transfer) are covered. Minor gaps like cropping or deletion exist but are non-essential for the server's purpose.

  • Average 3.2/5 across 4 of 4 tools scored. Lowest: 2.6/5.

    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 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

  • Behavior1/5

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

    No annotations provided; description offers no behavioral details such as output format, side effects, or required permissions. Only restates purpose.

    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?

    Single sentence, efficient but too minimal. Could be expanded with key behavioral info without losing conciseness.

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

    Completeness1/5

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

    Lacks output schema and annotations. Does not explain return value or behavior. Severely incomplete for a tool with 3 parameters.

    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?

    Description adds minimal value to schema: 'multiple input images' is redundant with schema. The prompt description is less informative than the schema's detailed description. Missing schema coverage for images items not compensated.

    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 action (compose) and resource (new image), using multiple input images and a guiding prompt. It distinguishes from sibling tools: edit_image edits existing, generate_image creates from scratch, style_transfer applies style.

    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?

    No guidance on when to use this tool versus alternatives. Lacks explicit context or when-not-to-use conditions.

    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 provided, and description only restates the model name. Missing behavioral details like synchronicity, latency, rate limits, or cost implications.

    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?

    Single sentence that is front-loaded and to the point. Could include more detail without being verbose, but no wasted words.

    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?

    No output schema, no description of return value (e.g., URL, base64). Lacks context on capabilities of the model or when to prefer this over siblings.

    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 covers 100% of parameters. Description adds value by suggesting 'use photographic terms for photorealism' for prompt, and clarifying file extension for saveToFilePath.

    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?

    Description clearly states action ('generate'), resource ('image'), and method ('from text prompt using Gemini 2.5 Flash Image'). Distinguishes from sibling tools (compose, edit, style transfer).

    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?

    No guidance on when to use this tool vs siblings like compose_images or edit_image. No when-not or alternative scenarios mentioned.

    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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It only states that style is transferred, but omits important details such as potential image size/resolution constraints, output format, and whether the operation is deterministic or requires significant computation. This is insufficient for an AI agent to anticipate 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, well-structured sentence that efficiently conveys the core function. Every word contributes to understanding, with no unnecessary elaboration.

    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?

    Despite moderate complexity (4 parameters, nested images, no output schema), the description fails to provide adequate context. It does not specify the output format (e.g., generated image returned inline or saved to path), behavior when prompt is omitted, or any error conditions. This leaves significant gaps for an AI agent.

    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 50% (prompt and saveToFilePath have descriptions; baseImage and styleImage do not). The tool description adds no additional parameter meaning beyond the schema—it merely restates 'optional prompt'. Given the presence of nested image objects without detailed parameter descriptions, the description should have clarified expected image formats or requirements.

    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 uses a specific verb ('transfer') and clearly identifies the resources ('style image', 'base image'), making the tool's function unambiguous. It also distinguishes this tool from siblings like 'compose_images' or 'edit_image' by explicitly mentioning style transfer.

    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 the tool should be used for style transfer tasks, but it does not provide explicit guidance on when to use this tool versus alternatives (e.g., compose_images, edit_image). No usage restrictions or prerequisites are mentioned.

    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 full burden. It mentions the model matches original style and lighting, a useful behavioral trait, but omits potential side effects, limitations, or output details.

    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 extremely concise (two short sentences) with no extraneous information. It could be slightly improved by front-loading the core action, but it's very efficient.

    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 no output schema and no annotations, the description lacks information about the return value (e.g., whether the edited image is returned as base64 or saved to a path). This gap in completeness moderately hampers agent decision-making.

    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 baseline is 3. The description restates what is already in the schema (e.g., 'provide one input image via base64 or file path') without adding significant new meaning.

    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 verb (edit), resource (image), and method (prompt). It distinguishes from siblings like 'generate_image' and 'compose_images' by focusing on editing an existing image.

    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 does not explicitly state when to use this tool vs. alternatives. While sibling names provide context, the description itself lacks explicit 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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