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Server Quality Checklist

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  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of overlap or confusion. synthesize_speech clearly indicates its single purpose of converting text to audio.

    Naming Consistency5/5

    The tool name follows a clear verb_noun pattern and directly matches the server's TTS purpose. There are no conflicting naming conventions to assess.

    Tool Count3/5

    A single tool is reasonable for a narrowly scoped TTS server, but the surface feels thin because related capabilities like voice selection or streaming control are absent.

    Completeness3/5

    The core text-to-speech operation is covered, but the toolset lacks visible voice, format, or streaming options that would be expected in a realtime TTS service. It works for basic synthesis but leaves notable gaps.

  • Average 4.2/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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

  • Behavior4/5

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

    With no annotations provided, the description carries the full behavioral disclosure burden. It proactively discloses the unauthenticated CPU fallback, latency expectations, and output format. It does not mention rate limits or text-length constraints, but the core runtime behavior and authentication requirements are transparent.

    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 two sentences with no filler. The primary action is front-loaded, and the latency/auth caveat is delivered concisely. Every sentence adds either functional or operational 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?

    For a three-parameter TTS tool with no output schema, the description covers the essential context: what it does, what it returns, and current performance constraints. It could mention voice-id availability or input size limits, but the schema provides the parameter structure and the description provides sufficient operational 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 description coverage is 100%, so the baseline is 3. The description does not add any parameter-specific meaning beyond the schema; it focuses on overall tool behavior. Since the schema already documents text, speed, and voice, the lack of additional detail is acceptable.

    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 opens with a specific verb and resource: 'Convert text to spoken audio using a real-time, streaming Kokoro-82M TTS gateway.' It also states the concrete output ('Returns a playable WAV file'), making the tool's function unmistakable even without sibling tools.

    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?

    The description provides clear operational context: it is unauthenticated, requires no API key, and has multi-second latency. This implicitly tells an agent when use is acceptable (when latency is tolerable and no auth is available), though it does not explicitly discuss alternatives or exclusions since no siblings exist.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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