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Komorebi-yaodong

Ketchup Draw MCP Server

by Komorebi-yaodong
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JavaScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.4

  • Disambiguation5/5

    The two tools have clearly distinct roles: optimize_prompt refines a prompt, while generate_image produces images from prompts. There is no overlap in functionality.

    Naming Consistency5/5

    Both tools follow the same verb_noun pattern: optimize_prompt and generate_image. This is consistent and predictable.

    Tool Count3/5

    With only two tools, the server feels minimal. While the tools cover a basic prompt-to-image workflow, the count is borderline for a full-featured server.

    Completeness4/5

    The core workflow of optimizing a prompt and generating an image is covered. Minor gaps exist, such as no options for adjusting generation parameters, but the essential functionality is present.

  • Average 3.9/5 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

  • 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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It does add the useful fact that each prompt generates exactly one image, but it omits critical details such as what the tool returns (e.g., image URLs), potential time delays, rate limits, or failure behavior. For a generation tool, this is insufficient transparency.

    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 extremely concise: two short sentences with no redundant information. Every phrase adds meaning, and the most important detail (the one-to-one mapping) is front-loaded. This is an model of efficient writing.

    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?

    With no annotations and no output schema, the description is expected to provide more context. It adequately explains the core function and the prompts parameter behavior, but it fails to mention the return value format, any side effects, or when to use this tool relative to 'optimize_prompt'. For a straightforward 2-parameter tool, the description is minimally adequate but not complete.

    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 input schema already provides full descriptions for both parameters (prompts and ratio), so the baseline is 3. The description adds value by clarifying that the prompts array maps one-to-one to generated images, which is not explicitly stated in the schema. This enrichment justifies a score above baseline, though it does not deeply explain ratio semantics.

    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: generating images from a list of prompts. It uses a specific verb ('generate') and resource ('images'), and the second sentence adds the key behavior that each prompt yields one image. This distinguishes it from the sibling tool 'optimize_prompt', which focuses on prompt refinement rather than image generation.

    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 when image generation is needed from text prompts, but it provides no explicit guidance on when to prefer this tool over 'optimize_prompt' or when not to use it. There is no mention of prerequisites, alternative workflows, or exclusions. The context is clear but not elaborated.

    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 states the transformation behavior (simple to detailed) and mentions 'using Ketchup AI', but it does not disclose return format, side effects, permissions, or rate limits. For a simple transformation this is minimal but not rich behavioral context.

    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 sentence of 13 words, front-loaded with the verb 'Optimize', and contains no redundant or filler text. Every word adds value.

    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 simple one-parameter tool with no output schema, the description adequately explains the purpose and implies the return value (the optimized prompt). It could mention the output format explicitly, but the statement 'into a detailed professional prompt' covers the essential outcome, making it complete enough for the tool's simplicity.

    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 description coverage is 100% for the only parameter 'prompt', with an example. The description adds the term 'drawing' but does not materially go beyond the schema's meaning. Baseline of 3 is appropriate due to high schema coverage.

    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 'Optimize' and a resource 'simple drawing prompt' with a clear goal of producing a 'detailed professional prompt'. This distinguishes it from the sibling tool generate_image, which creates images rather than enhancing prompts.

    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 clearly implies when to use the tool: when you have a simple prompt and need a more detailed one. It does not explicitly name alternatives or exclusions, but the context of the sibling generate_image suggests a workflow of optimizing first then generating. This meets the 'clear context, no exclusions' level.

    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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