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Server Quality Checklist

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  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool targets a distinct stage of the video pipeline: ingest, VFX generation, preview verification, and final render. There is no overlap in functionality.

    Naming Consistency5/5

    All tools follow the consistent pattern 'omni_video_<verb>', using clear action verbs (ingest, generate_vfx, preview, render) that accurately describe their purpose.

    Tool Count5/5

    With 4 tools, the server is well-scoped for a video processing pipeline. Each tool serves a critical, non-redundant role, making the set neither too sparse nor too heavy.

    Completeness4/5

    The pipeline covers ingest, VFX, preview, and render, but lacks independent tools for editing (e.g., trimming) or managing overlays directly. However, the render tool bundles many operations, minimizing gaps.

  • Average 3.7/5 across 4 of 4 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?

    With no annotations provided, the description must carry the full burden of behavioral disclosure. It only mentions output format and path but does not discuss side effects (e.g., file creation/deletion), potential costs, or state changes. This is insufficient for a tool that likely creates files.

    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 two sentences long, front-loading the primary action and output. Every word adds value; there is no redundancy or irrelevant detail.

    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 complexity of rendering motion graphics, the description covers key aspects: input type (HTML/CSS), output format (transparent .mov/.webm), and the fact that it uses Hyperframes. The presence of an output schema likely details return values further. Minor omission: no mention of constraints like size limits or supported CSS features.

    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 input schema has nested parameters with descriptions in $defs, but the top-level parameter 'request' lacks description. The tool description adds context about motion graphics but does not enhance understanding of individual parameters beyond what the schema already provides. Schema description coverage is 0% at top level, but nested descriptions exist, 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 that the tool renders motion graphics using Hyperframes, specifies examples (lower thirds, titles), and returns a path to a rendered transparent file. This distinguishes it from siblings like omni_video_ingest, preview, and render.

    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 explicit guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or conditions under which this tool should be chosen over siblings.

    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?

    With no annotations provided, the description carries the full burden. It lists operations performed and states it returns a path, but does not disclose side effects like file overwrites or authorization needs. This is adequate but lacks depth.

    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, well-structured sentence that front-loads the core purpose and lists operations concisely. Every word adds value without redundancy.

    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 sibling tools and presence of an output schema, the description adequately covers the tool's role and output. It could mention prerequisites or error conditions but is mostly complete for a render operation.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema coverage is 0% for the top-level parameter, though nested fields have descriptions. The description mentions operations that map to parameters (e.g., LUT grading to lut_path) but does not explain the parameters themselves or add meaning 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 clearly states the tool orchestrates the final render pipeline, listing specific operations like EDL cuts, LUT grading, and subtitle burning. It distinguishes from siblings by indicating this is the final step after ingestion and generation, making its role 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 implies usage as the final render step but does not explicitly state when to use it over alternatives or when not to use it. There is no mention of prerequisites or exclusions, leaving the agent to infer from sibling names alone.

    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?

    With no annotations provided, the description carries the full burden. It discloses the main actions (ingest, transcribe, construct graph, return metadata) but does not detail side effects (e.g., file modifications, resource usage, error behavior, permissions). Context like 'generates' and 'constructs' suggests non-destructive creation, but more depth is needed.

    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?

    Two concise sentences, front-loaded with the core action, followed by additional processing details and output. Every sentence is substantive and non-redundant.

    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 of moderate complexity, the description covers the main functionality and output. An output schema exists, so return value details are assumed to be structured. It lacks information on processing time, error handling, or system requirements, but the core completeness is adequate.

    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 description does not mention the single parameter 'directory_path', but the input schema provides a clear description. The context signal indicates 0% schema description coverage (though the schema actually has a description), so the tool description adds no parameter meaning beyond the schema. The parameter is simple and inferable from the tool's purpose.

    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 verb 'ingests' with a specific resource (directory of video files), and outlines the processing steps (transcripts, scene graph) and output (metadata path). It distinguishes from siblings (VFX, preview, render) by indicating this is the intake step.

    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 use for initial ingestion and processing, but provides no explicit guidance on when to use this tool versus alternatives (e.g., if only transcripts are needed), nor does it mention prerequisites or constraints like supported video formats.

    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 discloses that a PNG file is generated and returns its path, but does not clarify if the operation is read-only, if it modifies any files, or any side effects like temporary file cleanup. The behavioral disclosure is adequate but incomplete.

    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 concise sentences: the first states the core action, the second gives usage context, and the third specifies the return value. Every sentence adds value with no redundancy.

    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 tool's low complexity (3 inputs via one object) and the existence of an output schema, the description covers the essential purpose, use cases, and return value. It lacks details on the filmstrip format (e.g., number of frames, dimensions), but the output schema can provide that.

    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 input schema already provides descriptions for all nested parameters (file_path, start_time, end_time), so the description adds no additional parameter meaning. Since schema_description_coverage is effectively high for the actual inputs, a baseline score of 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 it generates a filmstrip PNG for visual verification of cut boundaries or B-roll placement. This verb+resource combination is distinct from sibling tools like omni_video_generate_vfx (adding effects) and omni_video_ingest (importing video).

    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 explicitly identifies two specific use cases (verifying cut boundaries or B-roll placement), providing clear context for when to use the tool. However, it does not mention when not to use it or suggest alternatives.

    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 there are no obvious security issues.
  • Evaluate tool definition quality.

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