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uraoz

Bouyomi-chan MCP Server

by uraoz
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Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'read_text' has a singular, clear purpose that cannot be confused with any other tool in this set.

    Naming Consistency5/5

    A single tool inherently exhibits perfect naming consistency, as there are no other tools to compare it against. The name 'read_text' follows a clear verb_noun pattern, which is consistent with itself.

    Tool Count2/5

    A single tool for a text-to-speech server is too minimal for practical use. While it covers the core functionality, typical MCP servers benefit from additional tools (e.g., for configuration, status checks, or voice control), making this count feel thin and limiting for agent interactions.

    Completeness3/5

    The tool 'read_text' provides the essential action for a text-to-speech server, but there are notable gaps. Missing operations might include stopping speech, adjusting speed or volume, checking status, or managing voice settings, which could hinder agent workflows in more complex scenarios.

  • Average 3.1/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 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 full burden. It mentions the reading happens 'with a monotone voice' which is useful behavioral context, but doesn't disclose other important traits: whether this is a synchronous or asynchronous operation, what happens with long texts, error conditions, or what the output actually is (audio file, playback, etc.). For a tool with zero annotation coverage, this is insufficient.

    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, efficient Japanese sentence that directly states what the tool does. Every word earns its place: 'テキスト' (text), '棒読みちゃん' (monotone voice), '読み上げます' (reads aloud). No wasted words or 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?

    Given the tool has no annotations, no output schema, and zero parameters, the description should provide more complete context. While it states the basic purpose, it doesn't explain what form the output takes (audio stream, file, immediate playback), performance limitations, or error handling. For a text-to-speech tool, this leaves significant gaps in understanding how to use it effectively.

    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 tool has zero parameters, and schema description coverage is 100%. The description doesn't need to explain parameters, and it correctly implies text input through its purpose statement. Baseline for zero parameters with full schema coverage is 4, as there's nothing to compensate for.

    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's purpose: 'テキストを棒読みちゃんで読み上げます' translates to 'Reads text aloud with a monotone voice'. This specifies the verb ('reads aloud') and resource ('text'), though it doesn't need to distinguish from siblings since none exist. The mention of 'monotone voice' adds specificity about the reading 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?

    The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for usage, or any exclusions. While no sibling tools exist to differentiate from, it lacks basic usage context like input format expectations or performance characteristics.

    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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