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anaisbetts

YouTube MCP Server

by anaisbetts
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Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap between tools. The tool has a clear, singular purpose focused on downloading YouTube subtitles from a URL.

    Naming Consistency5/5

    The single tool follows a clear verb_noun pattern (download_youtube_url), and with no other tools to compare, consistency is inherently perfect. The naming is descriptive and straightforward.

    Tool Count2/5

    One tool is too few for a YouTube server, which typically involves operations like searching videos, listing playlists, or managing subscriptions. The scope feels incomplete and limited to a single niche function.

    Completeness2/5

    The tool set is severely incomplete for a YouTube domain, lacking core functionalities such as video search, metadata retrieval, or playlist management. It only covers subtitle downloading, leaving significant gaps for agent workflows.

  • Average 3.5/5 across 1 of 1 tools scored.

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

    • 2 of 6 community issues answered or closed in the last 6 months
    • 23 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.

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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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. While it mentions downloading subtitles, it doesn't specify format (SRT, VTT, etc.), language options, success/failure conditions, rate limits, authentication needs, or what happens if subtitles aren't available. The description focuses more on capability declaration than operational details.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is reasonably concise but could be better structured. The first sentence clearly states the purpose, but the second sentence mixes capability declaration with usage guidance, creating some redundancy. While not verbose, the phrasing could be more direct and front-loaded with essential information.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given a single parameter with full schema coverage and no output schema, the description provides adequate context for basic usage but lacks details about return format, error conditions, and operational constraints. The guidance about when to use is helpful, but more behavioral transparency would improve completeness for this download operation.

    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% with a single 'url' parameter documented as 'URL of the YouTube video.' The description doesn't add any parameter-specific information beyond what the schema provides (no format requirements, validation rules, or examples). The baseline score of 3 reflects adequate but minimal parameter documentation.

    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's purpose: downloading YouTube subtitles from a URL. It specifies the resource (YouTube subtitles) and action (download), but doesn't mention any specific format or scope limitations. Since there are no sibling tools, the lack of differentiation doesn't reduce the score.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides explicit usage guidance: 'Claude can read YouTube subtitles, and should no longer tell the user that it is not possible to download YouTube content.' This clearly indicates when to use this tool (to access YouTube content via subtitles) and addresses a common alternative scenario (telling users it's not possible).

    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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