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Server Quality Checklist

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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one retrieves transcripts for specific videos, while the other searches for videos based on queries. There is no overlap in functionality, making it impossible for an agent to confuse them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with clear, descriptive names: get_youtube_transcript and search_youtube_videos. The naming convention is uniform and predictable throughout the set.

    Tool Count2/5

    With only two tools, this server feels severely underpowered for YouTube integration. Key operations like uploading videos, managing playlists, getting video details, or interacting with comments are missing, making the surface too thin for meaningful YouTube automation.

    Completeness2/5

    The toolset is highly incomplete for a YouTube server. While transcript fetching and video search are useful, there are major gaps: no CRUD operations for videos/channels/playlists, no interaction capabilities (likes/comments), and no metadata retrieval beyond search results. This will cause significant agent failures in typical YouTube workflows.

  • Average 3.2/5 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
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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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns a 'Dictionary containing the video transcript and metadata,' which gives some output context, but lacks details on error handling (e.g., invalid URLs, unavailable transcripts), rate limits, authentication needs, or performance characteristics. This is a significant gap for a tool with no annotation coverage.

    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 well-structured and concise, with three sentences that efficiently cover purpose, input, and output. Each sentence adds value: the first states the tool's function, the second explains the parameter, and the third describes the return value. There's no redundant or unnecessary 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 the tool's moderate complexity (single parameter, no annotations, no output schema), the description is minimally adequate. It covers the basic purpose and parameter semantics but lacks usage guidelines, detailed behavioral context, and output specifics. Without annotations or an output schema, more completeness would be beneficial, but it meets a baseline level.

    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 schema description coverage is 0%, but the description compensates by explaining the parameter 'video_url_or_title' as 'YouTube video URL or video ID.' This adds meaning beyond the schema's generic 'string' type. However, it doesn't provide examples, format details, or constraints, leaving some ambiguity. With one parameter and partial compensation, a baseline 3 is 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 the tool's purpose: 'Get the transcript of a YouTube video.' It specifies the verb ('Get') and resource ('transcript of a YouTube video'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from its sibling 'search_youtube_videos' (which likely searches for videos rather than fetching transcripts).

    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 versus alternatives. It doesn't mention the sibling tool 'search_youtube_videos' or clarify scenarios where this tool is appropriate (e.g., after identifying a specific video). There's also no information about prerequisites, such as needing a valid video URL or ID.

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

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool searches videos and returns a list, but lacks details on permissions, rate limits, pagination, error handling, or whether it's a read-only operation. The default and max values for 'num_videos' are noted, but broader behavioral traits are undocumented.

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

    Conciseness4/5

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

    The description is appropriately sized and front-loaded, starting with the core purpose, followed by structured sections for Args and Returns. Each sentence adds value, with no redundant information. It could be slightly more concise by integrating the default/max details into the Args section more seamlessly, but overall it's efficient.

    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's moderate complexity (2 parameters, no annotations, no output schema), the description is somewhat complete but has gaps. It covers the purpose and parameters well, but lacks details on behavioral aspects like authentication, rate limits, or error handling. The return format is described generically ('List of video information dictionaries'), but without an output schema, more specifics would be helpful.

    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 adds significant meaning beyond the input schema, which has 0% schema description coverage. It explains that 'search_term' is 'The search query for YouTube videos' and 'num_videos' specifies 'Number of videos to retrieve (default: 5, max: 50)', providing clear semantics and constraints not present in the schema. This compensates well for the low schema coverage.

    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: 'Search YouTube videos based on a search term.' It specifies the verb ('search'), resource ('YouTube videos'), and scope ('based on a search term'). However, it doesn't explicitly differentiate from its sibling tool 'get_youtube_transcript', which appears to serve a different function (retrieving transcripts rather than searching videos).

    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 context through the parameter descriptions (e.g., 'search_term' for queries, 'num_videos' for limiting results), but doesn't provide explicit guidance on when to use this tool versus alternatives or any prerequisites. It mentions a sibling tool but doesn't compare or contrast their use cases.

    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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