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aahl

mcp-vods

by aahl
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

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

  • Latest release: v0.1.9

  • Disambiguation5/5

    Each tool has a distinct purpose: vods_search for searching content and vods_detail for retrieving details and play URLs. No functional overlap.

    Naming Consistency5/5

    Both tools follow a consistent snake_case pattern with 'vods_' prefix and a clear verb (search/detail).

    Tool Count4/5

    With only 2 tools, the server is minimal but covers the core needs of searching and retrieving details for VOD content. Slightly under-scoped but reasonable.

    Completeness4/5

    The set covers basic search and detail retrieval, which handles most user requests. Missing features like categories or recommendations are minor gaps.

  • Average 3.8/5 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
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is failing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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    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?

    No annotations are provided, so the description carries the full burden. It discloses the core function (getting details/playback address) but does not mention side effects, authentication, or error behavior. Adequate but with 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?

    A single, front-loaded sentence with no fluff. Every word contributes to the core 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?

    For a tool with 3 parameters, no output schema, and no annotations, the description is adequate. It covers the basic purpose but lacks detail on return format (e.g., what 'details' include). Complete enough for simple use.

    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 100%, so baseline is 3. The description adds no additional parameter meaning beyond the schema's descriptions. It does not elaborate on parameters like id, source, or episode.

    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 retrieves details and playback addresses for various video types (movies, TV series, etc.), using specific verbs and resource. It distinguishes from the sibling vods_search by focusing on detail retrieval.

    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 after searching (via parameter linking to vods_search), but lacks explicit when-to-use or when-not-to-use guidance. No alternatives are mentioned.

    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?

    There are no annotations provided. The description adds some behavioral info (retry on timeout, formatting advice for keyword) but lacks disclosure of any side effects, permissions, or response characteristics. For a search tool, the behavioral transparency is adequate but not rich.

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

    Conciseness3/5

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

    The description is reasonably short but includes redundant phrasing (e.g., listing multiple content types) and could be more concise. It front-loads the core purpose but includes examples that, while helpful, add length.

    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 lack of output schema, the description covers input constraints, retry behavior, and typical use cases adequately for a search tool. It does not specify return format but that is acceptable for a search. Mostly complete.

    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 100%, so baseline is 3. The description adds a useful constraint for the keyword parameter (no brackets, quotes), which goes beyond the schema. For the page parameter, it adds no extra meaning. Overall, minimal added semantics.

    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 it searches for various video content types (电影、电视剧、综艺节目等) and provides specific example queries. This distinguishes it from the sibling tool 'vods_detail' which likely retrieves details.

    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 includes explicit cues for when to use the tool, such as user phrases like '我想看《仙逆》最新一集', and advises retrying on timeout. However, it does not explicitly exclude alternative tools or cases where the tool should not be used.

    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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