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waxberry-dev

live-translate-mcp

by waxberry-dev
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Server Quality Checklist

75%
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  • Latest release: v0.1.7

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: health_check checks dependencies, translate_file handles file-based translation with local saving, and translate_speech provides real-time speech translation with base64 output. No overlap.

    Naming Consistency5/5

    All tool names use consistent snake_case naming with a verb_noun pattern (health_check, translate_file, translate_speech), making it easy to infer functionality.

    Tool Count4/5

    With 3 tools, the server is small but well-scoped for its purpose of live translation between English and Mandarin. The count is reasonable, though slightly on the lower end, covering health check, file translation, and speech translation.

    Completeness3/5

    The server covers the core workflow of speech translation and file conversion but lacks text translation, language configuration, or support for additional language pairs, which are notable gaps for a translation tool.

  • Average 3.8/5 across 3 of 3 tools scored.

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

    • 1 of 1 community issues answered or closed in the last 6 months
    • 21 commits in the last 12 weeks
    • Last stable release on
    • 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.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    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

  • Behavior3/5

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

    The description explains the core behavior: auto-language detection, bidirectional translation, and output components. However, it does not disclose potential side effects, required permissions, rate limits, or error handling. As there are no annotations, more safety context would be beneficial.

    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 extremely concise, using two well-structured sentences. The first sentence states the action and language pair; the second adds output details. No redundant or unnecessary words.

    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?

    The description sufficiently covers the tool's purpose, input format, and output structure (original text, translation, base64 audio). However, it does not specify that the translation is limited to English-Mandarin only, nor does it differentiate from the sibling translate_file, which may be relevant for similar use cases.

    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?

    The input schema already provides detailed descriptions for both parameters (audio_base64 format and sample rate defaults). The tool description does not add new information about the parameters beyond what the schema offers, so it meets the baseline for high schema coverage.

    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 translates speech between English and Mandarin Chinese, with automatic language detection and output of original text, translation, and synthesized audio. However, it does not distinguish itself from the sibling tool 'translate_file', which could potentially translate speech files or text.

    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 explicit guidance on when to use this tool versus its siblings (health_check, translate_file) or when not to use it. It does not mention any prerequisites, limitations, or preferred scenarios.

    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, description fully discloses behavior: auto-detects language, saves audio next to source, plays it, and returns text. No mentions of side effects like file overwrite, but overall transparency is good.

    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?

    Five sentences, front-loaded with purpose, but somewhat repetitive ('automatically detects...', 'saves and plays'). Could be tightened without losing clarity.

    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?

    Covers input format, language pair, output actions, and return value. Missing details like error handling or file size limits, but sufficient given tool simplicity and no output schema.

    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 already covers 'file_path' with recommendations (16 kHz, mono, 16-bit). Description adds minimal new info ('absolute path') beyond repeating schema content. Schema coverage is 100%, so 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?

    Description clearly states the tool translates speech audio between English and Mandarin Chinese using a WAV file. It specifies the input and output, and implicitly distinguishes from sibling 'translate_speech' by focusing on file translation.

    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?

    No explicit when-to-use or when-not-to-use guidance. It implies usage for translating speech audio files but doesn't compare to alternatives like 'translate_speech' or discuss prerequisites.

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

  • Behavior3/5

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

    The description lists the dependencies checked but does not detail what happens if a dependency is missing (e.g., error vs. warning), nor the return format. No annotations provided to supplement.

    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?

    Single sentence, front-loaded with purpose, no wasted words. Efficient and clear.

    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 zero-parameter tool, the description covers what is checked. Could mention output but not essential given simplicity.

    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?

    No parameters exist, so the description adds no parameter info. Baseline score of 4 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?

    The description clearly states the tool checks availability of three specific dependencies (Whisper, Piper, espeak-ng), distinguishing it from translation tools like translate_file and translate_speech.

    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?

    No explicit guidance on when to use this tool vs alternatives; usage is implied as a prerequisite check for translation operations.

    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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