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Komorebi-yaodong

oai-tts

by Komorebi-yaodong
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
TypeScript
Remote

Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have entirely distinct purposes: list_voices enumerates available voices, while text_to_speech synthesizes audio and uploads it. There is no plausible way to confuse them, and the descriptions make the boundary explicit.

    Naming Consistency4/5

    Both names use consistent snake_case, which is predictable. minor deviation: list_voices follows a verb_noun pattern while text_to_speech is a noun phrase (verb implied), so it is not a perfectly uniform convention.

    Tool Count3/5

    With only two tools the surface is thin even for a narrowly scoped TTS server, which could reasonably expose model listing, voice preview, or audio retrieval operations. It is not a mismatch, but it sits at the borderline of minimal coverage.

    Completeness4/5

    The core lifecycle (discover voices, then synthesize speech) is covered, including fallback behavior and defaults for voice/speed. Missing secondary operations like previewing a voice or listing models are gaps agents can work around.

  • 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

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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

  • Behavior4/5

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

    Annotations already declare readOnlyHint=false, openWorldHint=true, and idempotentHint=false, so the safety profile is covered. The description adds genuinely new behavior: the audio is uploaded to an external file host and only a public URL is returned, plus the env-var default for voice. No return-format or failure behavior beyond that, hence a 4.

    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?

    Three front-loaded sentences covering the call flow, the instructions knob, and the defaults. Mostly earns its place, though the voice/speed defaults slightly duplicate the schema.

    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?

    With no output schema, the description usefully specifies the return value (仅返回公开音频 URL) and the upload side effect, and annotations cover the mutation safety profile. Complete enough for correct invocation, missing only error/timeout behavior.

    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 description coverage is 100%, so all five parameters are already documented. The description restates the instructions semantics and the voice/speed defaults already present in the schema, adding little beyond it. Baseline 3 is appropriate.

    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?

    States a specific verb+resource (调用 POST /v1/audio/speech 生成语音) plus the non-obvious side effect of uploading to Urusai hosting. An agent can distinguish this from the sibling list_voices, which merely enumerates voices, without opening either schema.

    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?

    Explains how to use instructions (语气、情绪、节奏、风格) and the fallback behavior for voice and speed, which is useful context. However, it never states when to prefer this tool over list_voices or provides any when-not-to-use guidance, so routing is only implied.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior5/5

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

    Beyond the readOnlyHint/openWorldHint annotations, the description discloses the concrete endpoint and query shape (GET /v1/tts/voices?model=...), plus a full fallback policy: on upstream failure, timeout, or invalid response it silently returns built-in OpenAI voices and flags source=fallback. That fallback semantics is exactly the kind of non-obvious behavior an agent cannot get from the annotations.

    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?

    Two tightly packed sentences: the purpose first, then the call strategy and fallback contract. Every clause carries information and nothing is repeated from the title or schema.

    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?

    With no output schema, the description carries the return-value burden and does state that results are structured and include a source marker. It does not describe the shape of the voice entries themselves (identifiers, locale, gender), which the sibling text_to_speech would need, leaving a small gap.

    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 takes zero parameters, so the baseline is 4. The description still adds value by explaining that the model argument sent to the endpoint is derived from the current OAI_TTS_MODEL configuration rather than supplied by the caller, which clarifies why the schema is empty.

    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 names a specific verb and resource ('获取当前 OAI_TTS_MODEL 的可用音色') and scopes it to the configured model, so an agent immediately knows this enumerates TTS voices. It never names the sibling text_to_speech, so the distinction is inferred from the resource rather than stated, keeping it just short of a 5.

    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?

    Usage is only implied: an agent infers this is the tool to call before text_to_speech to discover a valid voice identifier. There is no explicit when-to-use statement, no when-not condition, and no named alternative, so it lands at the minimum-viable tier.

    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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