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kjranyone

manga-vision-mcp

by kjranyone
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Server Quality Checklist

67%
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  • Latest release: v1.4.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: analyze_image processes a single image, while compare_image evaluates multiple images. No ambiguity or overlap exists.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern: analyze_image and compare_image. The naming is uniform and intuitive.

    Tool Count4/5

    With only two tools, the set is minimal but appropriate for the narrow domain of image analysis and comparison. It is slightly below the typical 3-15 range yet not insufficient for the stated purpose.

    Completeness5/5

    The tool set covers the essential operations for a manga vision checker: analyzing a single image and comparing multiple images. No obvious missing functionality for the domain.

  • Average 3.9/5 across 2 of 2 tools scored. Lowest: 3.3/5.

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

    • No community issues in the last 6 months
    • 2 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

  • Behavior3/5

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

    No annotations are provided, so the description must convey behavior. It states that images are sent and compared/verified, implying a read-only analysis, but it does not explicitly mention side effects, data handling, or that it is non-destructive. The absence of any statement about side effects is acceptable for a vision inspection, but not fully transparent.

    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 concise and well-structured, providing a clear overview of the operation, examples, and mode/vlm specifics in a compact form. It avoids unnecessary filler while still conveying the core functionality.

    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?

    The description lacks information about the output format or response structure. Since there is no output schema, the description should explain what the tool returns (e.g., a comparison report, pass/fail result, or text explanation). It also does not address error conditions or edge cases (e.g., too many images), making the tool's expected behavior incomplete for an agent.

    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 descriptions already cover all 6 parameters with 100% coverage, including details like order of sources, mode semantics, and vlm slots. The description largely restates this information without adding new meaning beyond what is already in the schema, so it adds marginal value for parameter understanding.

    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: comparing and verifying multiple images, with labels A, B, C… and examples of use cases (reference image × generated candidates, same-spec candidate comparison). It is unambiguous and distinct from a single-image analysis tool.

    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 does not explain when to use this tool versus the sibling tool analyze_image. It lacks guidance such as 'use for comparing multiple images, analyze_image for single-image inspection.' The emphasis on modes provides some context but does not explicitly contrast with alternatives.

    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?

    Annotations are absent, so the description carries full weight. It reveals defaults (mode=conformance, vlm=primary), the meaning of nsfw and fallback slots, and the meaning of free mode. It does not disclose side effects or failure modes, but this is a read-only analysis tool, so risks are limited.

    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 compact and front-loaded with the core purpose, followed by parameter clarifications in a consistent structure. The parenthetical-heavy style is information-dense but slightly dense with specification text; still every sentence 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?

    Given the simple schema (flat, 6 params, 2 required) and no output schema, the description covers the main aspects: purpose, defaults, parameter semantics. It lacks explicit return-value behavior or error conditions, but for an analysis tool this is a minor gap.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    All parameters are described in Japanese with meaningful detail: source format, prompt content guidance, enum semantics for mode and vlm, max_tokens bounds and default, max_long_edge meaning including the 0=unlimited case and env inheritance. This goes well beyond the JSON schema.

    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 states an exact verb ('解析する'), a specific resource ('画像'), and its main use case ('生成仕様への準拠検査'). It also distinguishes from sibling compare_images by focusing on single-image analysis against a prompt/spec rather than comparison.

    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?

    Description explains when to use the default conformance mode and what to include in the prompt for conformance checks, plus VLM slot selection semantics. It does not explicitly contrast with the sibling compare_images, but the single-image vs comparison distinction is implicit.

    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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