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seigo-gace

Deterministic Japanese Parser MCP

by seigo-gace
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Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusing it with others. The tool's purpose is clearly stated as analyzing Japanese text, so there is no ambiguity between tools.

    Naming Consistency5/5

    The tool name 'analyze_japanese' follows a clear verb_noun pattern and is descriptively consistent with the server's purpose. Having only one tool means there are no inconsistencies to evaluate.

    Tool Count2/5

    A single tool feels too few for the apparent scope of a 'Japanese parser' that outputs intents, references, metaphors, contradictions, guard results, and Task Packets. This complexity would typically warrant separate tools for different analysis aspects or configuration, making the tool count inadequate.

    Completeness3/5

    The tool covers a comprehensive set of analyses in a single operation, but the lack of granular tools means agents must run the full analysis every time, with no way to request specific components. This is a notable gap for flexibility and could hinder workflows that only need a subset of the output.

  • Average 3.3/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 105 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

  • Behavior3/5

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

    No annotations are provided, so the description must convey behavioral traits. It adds the useful trait 'deterministically' and lists the output structure, but it does not disclose whether the tool has side effects, requires authentication, or any limits. This is a moderate addition, not a full disclosure.

    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, front-loaded sentence with no fluff. Every phrase contributes to the understanding of the tool's function (deterministic analysis, input language, output categories). It is appropriately concise.

    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 having an output schema, the tool has 7 input parameters with zero documentation in either the schema or the description. The description only states the high-level purpose and output categories, leaving the agent without critical information about how to set parameters like analysis_depth, execution_mode, or known_entities. This is substantially incomplete for correct invocation.

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

    Parameters1/5

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

    Schema description coverage is 0% and the description does not explain any of the 7 parameters (e.g., original_text, analysis_depth, execution_mode). The description only lists output categories, providing no meaning for the input parameters, so the description fails to compensate for the lack of schema descriptions.

    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 ('analyze') with a clear resource ('Japanese text') and enumerates concrete output categories (intents, references, metaphors, contradictions, guard results, ordered Task Packets). This makes the tool's function unambiguous, even without sibling tools to differentiate.

    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 Japanese text analysis but provides no explicit guidance on when to use this tool versus alternatives or any exclusions. Since there are no sibling tools listed, the absence of explicit alternatives is not penalized heavily, but the description still lacks explicit usage context.

    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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