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Server Quality Checklist

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  • Latest release: v1.3.1

  • Disambiguation5/5

    Each tool has a distinct, non-overlapping purpose: generating a video, querying its status, and listing providers. An agent can easily select the correct tool based on the task at hand.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case: generate_video, query_video_status, list_providers. The naming convention is uniform and predictable.

    Tool Count5/5

    With only 3 tools, the set is tightly scoped to the core operations of video generation: create, monitor, and discover providers. Each tool is essential and earns its place without redundancy.

    Completeness4/5

    The lifecycle is largely covered: generation is initiated, status can be polled, and results downloaded. However, there is no explicit way to cancel or list past tasks, which is a minor gap but workable.

  • Average 3.7/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 7 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.

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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 only mentions querying and downloading, without revealing what happens if the task is incomplete, whether it blocks, what side effects downloading may have, or any error/edge-case behavior.

    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 sentence with no wasted words. It is appropriately sized and front-loaded, clearly stating the core action and object.

    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 absence of annotations and output schema, the description is insufficient for an agent to understand the full context. It does not explain return values, waiting behavior, or failure modes, leaving significant gaps for a tool that performs both status checking and downloading.

    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 the baseline is 3. The description adds no extra meaning beyond the schema; the link to output_directory is implied but already covered by the schema description.

    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's specific function: querying the status of a video generation task and downloading the result. It distinguishes itself from siblings (generate_video, list_providers) by focusing on status retrieval and result download.

    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 generating a video (task_id returned by generate_video), but does not explicitly state when to use it versus alternatives or any exclusions. The guidance is largely implicit through the schema rather than the description itself.

    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?

    No annotations are provided, so the description carries the full burden. It adds meaningful transparency by disclosing that no providers are configured, which is a significant behavioral limitation. However, it does not explain the asynchronous nature of video generation (evidenced by sibling query_video_status) or what happens after a successful generation (output location, status polling, or result format). This gap is notable for a generation tool with no annotations or output schema.

    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 front-loaded with the core purpose in the first sentence, and the second sentence efficiently conveys the provider limitation. It is appropriately brief, though 'Default: none' repeats information already in the schema (provider default). The 'none configured' statement is necessary for behavioral transparency, so the redundancy is minor. Overall it earns its place, but a slightly clearer tie-in to the provider default would be ideal.

    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?

    Despite having 7 schema-documented parameters, the description lacks essential lifecycle context. It does not mention that generation is likely asynchronous, that users should poll via query_video_status, or where the output video is saved. The disclosure that no providers are configured is a strong caveat, but it leaves the user wondering whether the tool returns an error immediately or attempts a fallback. Without annotations or an output schema, this is a significant completeness gap.

    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 description coverage is 100%, with all 7 parameters described in detail (e.g., image_url for ark/veo, duration options 5 or 10). The description adds high-level context (text vs image modes) that maps to prompt and image_url, but it does not provide per-parameter semantics beyond what the schema already states. Given full schema coverage, the description's contribution is minimal but non-zero, so baseline 3 is appropriate.

    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's function with specific verbs: 'Generate a video from a text prompt (text-to-video) or from an image + prompt (image-to-video, ark/veo).' It distinguishes two modes and names the resource (video). This is unambiguous and effectively differentiates from sibling tools like query_video_status and list_providers, which serve obviously different purposes.

    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 clear context on when to use the tool (for video generation) and distinguishes between text-to-video and image-to-video workflows. It also discloses the critical constraint 'Available providers: none configured. Default: none,' which implicitly advises that the tool is not actionable until providers are set up. No explicit exclusion against alternatives is needed since no sibling tool generates videos.

    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?

    No annotations are provided, so the description carries the full burden. It states the function but does not mention that the operation is read-only, whether authentication is required, or any side effects. For a simple list tool, this is acceptable but not rich in behavioral detail.

    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 a single, concise sentence that immediately conveys the tool's function without any unnecessary words or redundancy.

    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 simple list operation with no parameters and no output schema, the description fully captures the tool's purpose. The enumeration of provider categories gives sufficient context for an agent to decide when to invoke it.

    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 tool has zero parameters, so the baseline is 4 per the rubric. The description adds no parameter-specific details, but none are needed given the empty 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 uses a specific verb 'list' and clearly identifies the resource ('providers') with explicit categories (video, TTS, music). This distinguishes it from siblings like generate_video and query_video_status, making its 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 Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description does not provide explicit guidance on when to use this tool versus alternatives. However, the context of sibling tools clearly implies it is for enumerating available providers, so usage is inferred rather than directly stated.

    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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  • Evaluate tool definition quality.

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