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tulaman

youtube-watchlater-mcp

by tulaman
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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

    The two tools serve entirely different purposes: one retrieves the watch-later playlist, the other fetches subtitles for a specific video. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with 'get_' prefix, making the action and resource clear. No mixed conventions or irregular naming.

    Tool Count3/5

    With only two tools, the server feels thin for a YouTube-related service. The scope is narrow (watch later + subtitles), so the count is borderline but not extreme.

    Completeness2/5

    The watch-later domain lacks operations like removing or adding videos, and the subtitle tool is unrelated to the watch-later focus. The surface is minimal and leaves obvious gaps for a cohesive workflow.

  • Average 3.9/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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.

  • 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?

    With no annotations, the description carries full burden. It discloses that subtitles are auto-generated, that yt-dlp is used, and that the output is VTT content, which is useful context. However, it does not mention potential failures (e.g., subtitles unavailable, network errors), rate limits, or the need for YouTube cookies, which are notable behavioral aspects of this tool.

    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, well-structured sentence that front-loads the action and includes essential information (tool, method, output). No word is wasted, and it is easy to parse.

    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?

    The description provides a reasonable overview for a straightforward tool, but with no output schema, it could clarify what happens when subtitles are not found or how the VTT content is returned (e.g., as a file path, string). The complexity is moderate due to 4 parameters, but the description does not fully cover edge cases or the exact behavior of the browser/profile parameters.

    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%, and each parameter has a clear description (e.g., 'Subtitle language code', 'YouTube video URL or bare video ID'). The tool description adds little beyond this, but since the schema is complete, the baseline of 3 is appropriate. It does not explain how parameters interact (e.g., browser/profile for cookie access) beyond what schema already states.

    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 uses a specific verb ('Downloads') and clearly identifies the resource ('auto-generated subtitles for a YouTube video'), method ('via yt-dlp'), and output ('VTT content'). This distinguishes it from the sibling tool get_watch_later, which has a different purpose.

    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 when subtitles for a YouTube video are needed, and the inclusion of browser/profile parameters hints at cookie-based authentication, but it does not explicitly state when to use this tool instead of alternatives or provide exclusionary conditions. The alternative (get_watch_later) is unrelated, so no direct comparison is given.

    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?

    No annotations are provided, so the description must disclose behavioral traits. It mentions using yt-dlp and reading cookies from a local browser, which is a key behavioral detail. However, it does not explain caveats like cookie availability, browser being closed, or failure scenarios. Some value is added, but it is not comprehensive.

    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, efficient sentence that is front-loaded with the action and resource, then specifies the method. No redundant or filler content.

    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 list-retrieval tool, the description covers what it does and how (yt-dlp, local browser cookies). It lacks information about return format or error conditions, but given no output schema, it is reasonably complete. It could explicitly note that browser cookies must exist and the profile must match, which is only implied.

    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 schema already documents limit, browser, and profile adequately. The description itself adds no additional parameter information, so it relies on the schema, giving 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 ('Returns videos') and the specific resource ('your YouTube Watch Later list'), also mentioning the method (yt-dlp with cookies). This distinguishes it from the sibling tool get_subtitles, which is about subtitles, not watch later.

    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 clear context: it is for retrieving Watch Later videos. However, it does not explicitly mention when to use this tool instead of alternatives or any exclusions. Given the only sibling is get_subtitles, the distinct purpose is clear, but explicit guidance is missing.

    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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