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ZahiriNatZuke

whisper-transcribe-mcp

by ZahiriNatZuke
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Python
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Server Quality Checklist

75%
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  • Latest release: v1.1.2

  • Disambiguation5/5

    Each tool serves a distinct purpose: listing models, transcribing from base64, and transcribing from file. No overlap in functionality.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern using snake_case (list_models, transcribe_base64, transcribe_file).

    Tool Count5/5

    Three tools is well-scoped for a transcription server: one for configuration/model listing and two for handling different input formats (base64 vs file).

    Completeness4/5

    The tool surface covers the core transcription workflow with two input methods and model listing. Minor gap: no support for streaming or URL-based audio input.

  • Average 3.5/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 7 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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.

  • This repository includes a glama.json configuration file.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
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  ]
}

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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?

    The description does not disclose any behavioral traits beyond the basic action. It does not mention if the tool requires network access, writes temporary files, uses local or remote processing, or any error conditions. Since no annotations are provided, the description carries the full burden but fails to provide meaningful behavioral context.

    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?

    The description is a single, concise sentence that is immediately clear. It is appropriately front-loaded but could benefit from minimal structured details (e.g., supported formats) without becoming verbose. The extreme brevity does not hurt clarity but leaves some gaps.

    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?

    Despite the presence of an output schema and full parameter documentation, the description fails to provide context about when to use this tool vs. the sibling 'transcribe_base64', or about any side effects or limitations. For a tool with five parameters and multiple options, the description is insufficiently complete.

    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 has 100% description coverage, so the schema already documents all five parameters thoroughly. The tool description adds no additional semantic value beyond what is in the schema, resulting in a baseline score of 3.

    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 action ('transcribe') and the resource ('audio file'), immediately distinguishing the tool from its siblings 'list_models' (which lists models) and 'transcribe_base64' (which transcribes base64-encoded audio). The verb+resource combination is specific and unambiguous.

    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 its siblings or alternatives. It does not mention file size limits, audio duration constraints, or any prerequisites. Users receive no contextual advice on selecting this tool over transcribe_base64 or list_models.

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

  • Behavior2/5

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

    No annotations are provided, and the description does not disclose behavioral details such as backend used, internet requirement, output format, or limitations (e.g., audio length), relying solely on minimal description.

    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 wasted words, efficiently conveying the core purpose.

    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?

    Despite a rich input schema and available output schema, the description omits essential context such as return format, prerequisites, and backend behavior, making it incomplete for complex tool usage.

    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 description adds no additional meaning beyond what the input schema already provides; baseline score of 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?

    The description clearly states the tool transcribes audio from a base64-encoded string, distinguishing it from siblings like transcribe_file (file-based) and list_models.

    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 usage for base64 audio input but lacks explicit when-to-use, when-not-to-use, or alternative guidance, leaving room for confusion with transcribe_file.

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

  • Behavior2/5

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

    No annotations provided; description states it lists information but does not explicitly confirm read-only or side effects. Minimal behavioral context.

    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 with zero waste; front-loaded with action and resource.

    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?

    No parameters, output schema exists to describe return values; description fully covers purpose.

    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 in schema (0 count), baseline is 4. Description adds no param info, but none needed.

    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?

    Clear verb 'List' and specific resource 'available Whisper model sizes and current configuration'. Distinguishes from sibling tools transcribe_base64 and transcribe_file.

    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?

    Description implies usage for checking available models, but no explicit when-to-use or alternatives given.

    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 the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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