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BlockBenny

tubemcp

by BlockBenny
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.3

  • Disambiguation5/5

    The two tools have completely distinct purposes: one searches for videos and returns metadata, the other fetches a transcript for a specific video. There is no overlap or ambiguity.

    Naming Consistency4/5

    Both tools share the 'youtube_' prefix and use verb-like names, but 'youtube_get_transcript' follows a verb_object pattern while 'youtube_search' is just a verb. Minor deviation from a consistent pattern.

    Tool Count3/5

    With only two tools, the server is minimal but covers its core workflow of search and transcript retrieval. This falls into the borderline range for tool count.

    Completeness5/5

    The search and get_transcript tools provide a complete workflow: an agent can find a video and then fetch its transcript. There are no obvious missing operations for the stated purpose.

  • Average 4.5/5 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 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.

    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 provided, the description carries the full burden of behavioral disclosure. It mentions local caching and the segment structure of transcripts, adding useful context. However, it does not state whether the operation is read-only, potential errors, or authentication requirements.

    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 three sentences, each providing unique and necessary information: purpose, usage constraint, and result format. It is front-loaded and free of filler.

    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 tool with only one parameter and an output schema, the description covers the core purpose, usage context, parameter format, and even adds structural details about the transcript. It could mention error handling or rate limits, but these are not critical for a straightforward fetch operation.

    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 only defines video_url as a string, but the description clarifies that it accepts a URL or video ID, which is essential for correct invocation. With 0% schema description coverage, this added meaning significantly helps the agent.

    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 transcript and metadata for a single YouTube video, using a URL or video ID. It distinguishes itself from the sibling tool youtube_search by specifying the exact resource (transcript) and action (fetch).

    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 guidance to 'fetch only the videos most relevant to the user's question — avoid bulk fetching,' which helps the agent decide when to use this tool. It does not explicitly contrast with youtube_search, but the context is clear enough.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description carries the burden of disclosing behavior. It reveals that each query runs a separate search, results are deduplicated by video ID, and only metadata is returned. While it doesn't mention pagination or rate limits, it covers the key behavioral traits that affect how an agent would use the results.

    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 compact and well-organized: it states the main capability, explains how queries are executed, offers usage guidance, and specifies the output scope. Every sentence contributes meaningful information with no redundancy or fluff.

    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 search tool with only two parameters and an output schema present, the description is complete. It covers the purpose, usage strategy, behavioral nuances (deduplication, separate searches), and the nature of results (metadata only). There is no ambiguity about when or how to use this tool.

    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?

    Schema coverage is 0%, so the description compensates by explaining the query strategy behind the 'queries' parameter, including typical count and phrasing. However, 'max_results_per_query' is not mentioned in the description, though its name and default value are self-explanatory. The guidance on query angles adds value beyond the schema.

    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 begins with 'Search YouTube with multiple queries for broader coverage,' clearly specifying the verb (Search), resource (YouTube), and distinguishing feature (multiple queries). This differentiates it from the sibling tool youtube_get_transcript, making the purpose unambiguous.

    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?

    It explicitly advises using 2-3 queries from different angles, providing concrete strategies (specific, broader, alternative phrasing). It also states 'Returns metadata only — no transcripts,' indicating when this tool is not appropriate, especially compared to the transcript-focused sibling.

    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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