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pobibibi

youtube-transcript-mcp

by pobibibi
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Server Quality Checklist

67%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: transcribe_video retrieves and saves a transcript, while list_languages provides metadata about available captions. No overlap or ambiguity exists.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern: transcribe_video and list_languages. The naming is clear, predictable, and uniform.

    Tool Count3/5

    With only two tools, the server feels slightly thin even for its narrow domain. However, the two tools cover the core workflow of checking languages and transcribing, so the count is borderline but defensible.

    Completeness4/5

    The server covers the essential transcript retrieval cycle well. A minor gap is that transcribe_video does not return the transcript directly, requiring a follow-up file read, but the workflow is complete and workable.

  • Average 4.4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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 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.

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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?

    With no annotations, the description carries the burden. It conveys read-only behavior ('Show') and explicitly says it is not for transcribing. However, it does not discuss edge cases or limitations, so transparency is only basic.

    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 sentences with zero fluff. The first sentence delivers the main purpose, and the second gives a practical usage tip, making every word earn its place.

    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 one-parameter tool with no output schema, the description lists the expected output components (captions, language, duration, chapters), giving sufficient context without needing deeper details.

    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 single parameter url is fully described in the schema as 'YouTube video URL or bare video ID.' The description adds no extra parameter detail, so baseline 3 applies due to high schema coverage.

    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?

    Clearly states a specific verb ('Show') and resource ('which captions a YouTube video has'), listing the exact metadata returned. Distinguishes itself from the sibling transcribe_video by positioning itself as a pre-transcription check.

    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?

    Provides clear context: use before transcribing when unsure if the desired language exists. It does not explicitly state when not to use or name alternatives, but the guidance is unambiguous.

    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?

    With no annotations, the description carries the full burden, and it excels: it discloses that the transcript is not returned by default, that a file is saved, the summary structure, the language fallback behavior, and the reliability caveat for machine translations. This is thorough and prevents false expectations.

    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 dense paragraph but remains readable and every sentence earns its place. It could be slightly more structured with bullets to improve scannability, but it is not unnecessarily verbose.

    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?

    For a complex tool with 8 parameters, a file-saving side effect, and no output schema, the description covers the default return value, summary fields, file path resolution, language handling, timestamps, chapter behavior, and description toggles. No critical gaps are apparent.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema coverage is 100%, but the description adds substantial meaning: it explains the machine-translation caveat for language, the token cost of return_text, the block_seconds behavior in the absence of chapters, and the default folder resolution. These insights are not evident from the schema alone.

    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 "Transcribe" and the resource (YouTube video), and specifies the output format: an LLM-ready Markdown file with front matter, chapter index, and timestamped paragraphs. It distinguishes itself from the sibling tool list_languages, which is about language codes, not transcription.

    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 explicit usage context: it explains the default behavior (does not return transcript, saves file path and summary), how to request a language, and the trade-offs of return_text. It doesn't explicitly name list_languages as a prerequisite, but the language-code context implies its use, and the guidance on when to avoid machine translation is clear.

    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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