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yunlinwu

youtube-transcript-mcp

by yunlinwu
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

    Each tool has a clear, distinct purpose: one fetches the full transcript, the other searches within it. No functional overlap.

    Naming Consistency3/5

    Both use snake_case but the naming pattern is inconsistent: 'get_transcript_tool' includes a redundant '_tool' suffix while 'search_transcript' does not.

    Tool Count5/5

    Two tools are well-scoped for the server's focused purpose of YouTube transcript access and search. No unnecessary tools.

    Completeness4/5

    The tool surface covers the core operations (full transcript retrieval and search) but lacks auxiliary features like language detection or transcript availability checking.

  • Average 3.3/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 5 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

  • Behavior2/5

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

    With no annotations, the description carries full burden for behavioral disclosure. It mentions context_seconds but omits details about output format, timestamps, rate limits, or authentication needs. The minimal description leaves significant behavioral gaps.

    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 very concise: one sentence summary followed by a bulleted list of arguments. Every sentence adds value with no redundancy. Structure is clear and scannable.

    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 the tool has 4 parameters, no annotations, and an output schema exists, the description covers basic requirements. However, it does not address error conditions, prerequisites (e.g., video must have captions), or the structure of returned segments, leaving some gaps.

    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 coverage is 0%, so the description must compensate. It provides brief explanations for all four parameters (url, query, lang, context_seconds) that add meaning beyond property names, but the explanations are minimal and lack format constraints or examples.

    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 searches a YouTube video's transcript for matching segments, using specific verb and resource. It distinguishes from the sibling get_transcript_tool by implication (search vs. full retrieval), but does not explicitly contrast them.

    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?

    No guidance is provided on when to use this tool versus the sibling get_transcript_tool or any alternatives. The description simply states what it does without usage context.

    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?

    The description discloses key behavior: obtaining transcript with timestamps and optional Whisper fallback. However, it omits details about authentication, rate limits, or error handling when captions are unavailable (e.g., falls back to Whisper only if flag is true). With no annotations, the description could be more explicit about edge cases.

    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 exceptionally concise with no redundant information. The main purpose is stated upfront, followed by a clear parameters list. Every sentence serves a purpose.

    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 presence of an output schema (which likely documents return format), the description covers the essential inputs and fallback behavior. It could mention that the result includes timestamps inline, but overall it is sufficiently complete for a simple retrieval 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?

    The description compensates for zero schema coverage by explaining each parameter's purpose, defaults, and usage. For example, 'url' is described as 'YouTube video URL or video ID', and the fallback parameter's behavior is clarified. Adding example values could improve it further.

    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 retrieves the full transcript with timestamps from YouTube. While it distinguishes itself from the sibling 'search_transcript' by focusing on full retrieval, it does not explicitly differentiate use cases.

    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?

    No guidance is provided on when to use this tool versus the sibling 'search_transcript'. The description only explains parameters but lacks context for tool selection.

    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 the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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