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ZubeidHendricks

YouTube MCP Server

by ZubeidHendricks
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TypeScript
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Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.2

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap between tools, so disambiguation is perfect.

    Naming Consistency2/5

    The single tool name 'videos_searchVideos' mixes camelCase and an awkward verb_noun structure ('videos' as prefix then 'searchVideos'), showing internal inconsistency and lack of a clear pattern.

    Tool Count1/5

    One tool is far too few for a YouTube MCP server, which typically requires multiple operations (search, video details, channel info, etc.), making the tool count extremely inadequate.

    Completeness1/5

    The tool only supports searching for videos, missing critical operations like retrieving video details, listing playlists, or managing subscriptions, leaving severe gaps in covering the YouTube domain.

  • Average 2.7/5 across 1 of 1 tools scored.

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

    • 1 of 2 community issues answered or closed in the last 6 months
    • 10 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.

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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?

    There are no annotations, so the description must disclose behavioral traits. It only says 'Search for videos,' omitting any details about rate limits, pagination, return format, or side effects. This is insufficient for an agent to anticipate behavior.

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

    Conciseness2/5

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

    The description is a single sentence, which is too brief for a tool with 13 parameters. While concise, it is under-specified and does not earn its place; it should include more context.

    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?

    Given the tool's complexity (13 parameters, no output schema), the description lacks completeness. It does not explain return values, default behaviors, or interaction effects between parameters, leaving the agent underinformed.

    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 itself documents each parameter. The tool description adds no additional meaning beyond the schema, making the baseline score of 3 appropriate.

    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 a specific verb ('Search') and resource ('videos on YouTube'), making the tool's purpose immediately understandable. However, it lacks any differentiation from potential sibling tools (none provided) and is somewhat generic.

    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, when not to use it, or any alternatives. With no sibling tools mentioned, the description could at least imply typical search scenarios, but it does not.

    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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Glama performs regular codebase and documentation scans to:

  • 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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