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warshanks

yt-transcript-mcp

by warshanks
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only a single tool, there is no potential for confusion between tools. The purpose of get_youtube_transcript is clearly defined and specific to fetching YouTube transcripts.

    Naming Consistency5/5

    The tool name follows the standard verb_noun pattern (get_youtube_transcript), which is consistent and descriptive. Even though there is only one tool, the naming convention is appropriate.

    Tool Count4/5

    The server's scope is narrowly defined as retrieving YouTube transcripts, and a single tool covers this purpose effectively. While the count is below the typical 3-15 range, it is not excessive or insufficient for such a focused domain.

    Completeness5/5

    The tool fully addresses the domain of fetching and cleaning YouTube transcripts, including fallback from manual to auto-generated subtitles. There are no obvious missing operations for a transcript-fetching service.

  • 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
    • 0 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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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?

    Without annotations, the description notes the fallback strategy from manual to auto-generated subtitles, which is a behavioral trait. However, it does not explain what 'cleans' entails, potential failure modes, or the output format.

    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, directly addressing the core function and the fallback behavior without extraneous information.

    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?

    Given the tool's simplicity (one parameter) and the presence of an output schema, the description is largely complete. It covers the primary functionality and fallback, though it could mention limitations like availability or language support.

    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 schema provides only a parameter named 'url' with no description. The tool description clarifies that this parameter expects a YouTube video URL, adding essential meaning that the schema lacks.

    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 fetches and cleans YouTube transcripts using yt-dlp, with a specific verb and resource. It also specifies fallback behavior, making it distinct and purposeful even without siblings.

    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?

    While there are no sibling tools to differentiate from, the description implies usage for YouTube video transcript retrieval. It does not explicitly state when not to use it, but the context is clear enough to guide an agent.

    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.
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