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tungtuantran

gemini-tts

by tungtuantran
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Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools are entirely distinct: one generates speech, the other lists generated files. There is no overlap or ambiguity in their purposes.

    Naming Consistency5/5

    Both tools follow the verb_noun pattern: generate_speech and get_audio_files. The naming is consistent, clear, and predictable.

    Tool Count3/5

    Two tools is on the thin side for a TTS server, but they cover the core generate and retrieve workflow. It feels slightly minimal rather than unreasonably sparse.

    Completeness4/5

    The server covers the essential TTS lifecycle: generating speech and listing outputs. Minor gaps exist such as no delete operation or voice selection flexibility, but agents can work around them for basic use.

  • Average 4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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

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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?

    There are no annotations to describe safety or side effects, and the description only says 'List all WAV audio files.' It does not explicitly state that the operation is read-only, nor does it disclose any permissions or potential side effects. The description carries the full burden here and falls short.

    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, clear sentence with no unnecessary words. It effectively conveys the tool's purpose without verbosity.

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

    Completeness5/5

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

    Given the tool's simplicity (no parameters, no output schema, and a straightforward list operation), the description is complete. It tells the agent exactly what the tool does, which is sufficient for calling it correctly.

    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 no parameters, so the baseline score is 4. The description does not need to add parameter meaning because there is nothing to describe.

    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 'List' and the resource 'all WAV audio files generated by the Gemini TTS MCP.' It is specific about the tool's scope and implicitly distinguishes it from the sibling tool generate_speech, which creates such files.

    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 mention when to use this tool versus the sibling generate_speech. While the function name and description make it obvious that this lists files and the sibling creates them, there is no explicit guidance or condition for choosing this tool over the alternative.

    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?

    With no annotations provided, the description carries the full burden. It discloses key behavioral outcomes: the audio is generated via Gemini TTS, uses a specific default voice, and is saved as a WAV file. It could mention return behavior or file location, but the core side effect is explicit.

    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 concise, front-loaded with the main action, and contains no redundant or filler text. The extra voice detail is relevant and placed naturally.

    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 generation tool with no output schema, the description is mostly complete: it states the action, default voice, output format, and optional filename. It does not specify the returned value or default file name behavior, but this is a minor gap given the sibling get_audio_files context.

    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 coverage is 100%, so the baseline is 3. The description adds helpful color about the default voice and output format, but the parameters themselves are already well documented in the 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?

    The description clearly states a specific action ('Generate natural English narration audio') and resource ('using Gemini TTS'), and differentiates from the sibling tool get_audio_files by focusing on creation rather than retrieval.

    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 when to use the tool—when speech audio needs to be generated—but does not explicitly contrast it with get_audio_files or state when not to use it. Usage is inferable but not directly guided.

    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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