Server Quality Checklist

Tool Scores

• job_cancelA
  3.8/5.0
  Behavior4/5

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

  With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently explains the two possible outcomes (removal if queued, interruption if running), which is valuable. It does not cover edge cases like already-completed jobs or error handling, but for a cancel action, this level of detail is reasonable.

  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 concise two-clause sentence: 'Cancel an H3 job: removes it if still queued, interrupts it if running.' Every word adds value, with no redundant information or filler.

  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?

  The tool is simple with one parameter and no output schema. The description explains the core behavior but omits details about return values, error scenarios, or job states like 'completed'. While not critical for a cancel tool, the absence of these details leaves the agent with incomplete expectations.

  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?

  The description does not explain the 'prompt_id' parameter beyond its name, and the schema provides no descriptions (0% coverage). The parameter name is somewhat self-explanatory, but the description fails to clarify its format, origin, or how to obtain it. This leaves a gap in agent understanding.

  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 purpose: 'Cancel an H3 job'. It also specifies behavior based on job state ('removes it if still queued, interrupts it if running'), which distinguishes it from sibling tools like job_status and h3_image_to_video. The verb+resource structure makes the action 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 the primary use case: cancel a job. However, it does not explicitly mention when to choose this tool over alternatives (e.g., job_status to check status first) or any exclusions (e.g., cannot cancel completed jobs). The context is clear but lacks explicit guidance.

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

• job_statusA
  3.8/5.0
  Behavior3/5

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

  With no annotations, the description carries the full burden. It discloses that the tool returns status and output file paths post-completion, and the verb 'Check' implies a read-only operation. However, it does not disclose behavior on error, missing IDs, or explicitly confirm no side effects, so it is minimally transparent.

  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 one concise sentence that front-loads the purpose ('Check a submitted H3 job') and immediately follows with the return values. Every word earns its place 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?

  The tool is simple with one parameter and no output schema. The description covers the core purpose and return values (statuses and output paths), which is sufficient for a straightforward status check. However, it omits error handling details, which prevents a perfect 5.

  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 has 0% coverage, and the description only mentions 'submitted H3 job' which implies prompt_id is the job identifier but does not explicitly define it. For a single parameter, this provides some additional meaning over the schema's bare 'Prompt Id' label, but it does not fully compensate for the lack of 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 'Check a submitted H3 job' with a specific verb and resource, and it distinguishes itself from siblings like job_cancel (cancel) and job_preview (preview) by focusing on status retrieval. It also lists the exact statuses returned.

  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 when you want the status of a submitted H3 job, but it does not explicitly state when not to use it or mention alternatives. It provides clear context but lacks exclusionary guidance, so it falls short of a 4.

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

• job_previewA
  4.1/5.0
  Behavior4/5

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

  Discloses that MCP has no video content type, so the tool returns a contact sheet of frames plus audio instead. This is important behavioral context not available from annotations (which are absent). It implies a read-only operation and describes the return format.

  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 paragraphs with the purpose in the first sentence. The additional details about the output format and usage context are relevant and not 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?

  Given no output schema, the description explains the return format (contact sheet + audio) and the reason for this design. It doesn't describe each parameter in detail, but the tool is relatively simple and the core behavior is covered.

  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?

  The schema has no parameter descriptions (0% coverage), and the description does not explain the meaning of rows, columns, tile_width, or include_audio. Parameter names are somewhat self-explanatory, but the description adds no detail, leaving the agent to infer from names alone.

  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 purpose: to look at and listen to a finished H3 job. It differentiates from siblings like job_status and job_cancel by focusing on previewing results and explains the output format.

  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?

  Explicitly says to use it to judge a result before iterating on the prompt, indicating appropriate timing. It does not name alternatives or when-not-to-use scenarios, but the context is clear enough for selection.

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

• h3_reference_to_videoA
  4.3/5.0
  Behavior4/5

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

  With no annotations, the description discloses key behaviors: the job is asynchronous ('Returns immediately with a prompt_id - poll job_status'), it takes minutes with an estimated_seconds field, and ref_image_size 'max' is 'several times slower.' It also warns about sage_attention redundancy. It doesn't cover permissions or failure modes, but the disclosure is strong for a generation tool.

  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 a dense single paragraph where every sentence adds value: purpose, syntax, limits, parameter trade-offs, use case, timing, and async behavior. While long, it is justified by complexity. Structure could be improved with bullets, but it is not wasteful.

  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 17-parameter tool with no annotations and no output schema, the description covers the critical invocation details: async flow, reference syntax, parameter trade-offs, and the cross-shot workflow. It doesn't explicitly say references are asset IDs from list_assets, but the sibling context implies it. Overall, sufficient for correct use.

  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?

  Despite 0% schema coverage, the description explains the most important parameters: positional refs in prompt, max counts for ref arrays, ref_image_size trade-offs, width/height defaults, and sage_attention. It omits steps, scheduler, megapixels, and shift_* but these are secondary with defaults. The compensation is solid but not exhaustive.

  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 states a specific verb and resource: 'Generate video with synchronized audio from a prompt plus reference images, videos, and/or audio.' It also explicitly identifies this as the tool for 'CONSISTENCY ACROSS SHOTS,' which distinguishes it from the sibling h3_image_to_video. The positional reference syntax and model name add further specificity.

  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 a concrete use case workflow: generate shot 1, pull a frame, and pass it as a reference for subsequent shots. It also gives guidance on leaving width/height unset and checking list_assets before setting sage_attention. It does not explicitly contrast with h3_image_to_video, but the cross-shot scenario clearly implies when this tool is appropriate.

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

• h3_image_to_videoA
  4.4/5.0
  Behavior5/5

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

  With no annotations, the description fully discloses operational behavior: execution time ('~3-4 min at the defaults on an RTX 4090'), immediate return with a prompt_id, polling recommendation using estimated_seconds, the single-continuous-shot limitation, resolution defaults and trade-offs, frame length constraints, and the sage_attention redundancy caveat. This is exceptional transparency for a complex tool.

  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 longer than typical but every sentence earns its place. It is front-loaded with the core purpose, then delivers practical operational details, performance expectations, and caveats in a structured, scannable format. No redundancy or filler.

  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 15-parameter tool with no output schema, this description is remarkably complete. It covers the execution flow (returns prompt_id, poll job_status), performance, constraints, parameter defaults, and gotchas. The only minor omission is detailed error handling, but the provided context is sufficient for an agent to invoke and monitor the tool correctly.

  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?

  Schema description coverage is 0%, so the description compensates by explaining the most significant parameters: length (frame count, 24fps, grid snapping, range), width/height (defaults and advice), sage_attention (do not set without list_assets), and first_frame/last_frame (local path or input-folder name). However, several parameters like steps, seed, scheduler, sampler_name, megapixels, shift_audio/video, and filename_prefix are left unexplained, though many are conventional.

  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 a specific action: 'Generate video with synchronized audio from a text prompt, optionally anchored by a first and/or last keyframe (MiniMax-H3 fl2va model)'. It distinguishes between text-to-video and image-anchored modes but does not explicitly differentiate from the sibling tool h3_reference_to_video, which is a similar generation tool.

  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?

  Provides concrete usage context such as 'With no keyframes this is pure text-to-video' and warns against multi-shot use: 'cannot contain cuts, so for a multi-shot sequence call this once per shot and edit the clips together'. Also advises checking list_assets before setting sage_attention. Does not explicitly name alternative tools, but clear situational guidance is present.

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

• list_assetsA
  4.5/5.0
  Behavior4/5

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

  The description discloses the behavioral scope: it lists models and media files from the input folder, implying read-only access. With no annotations, this is valuable context. It doesn't explicitly state 'read-only' or return format, but the description adequately conveys the tool's behavior for a simple list operation.

  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, front-loaded sentence that immediately states the action and scope. Every word earns its place with no 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 zero-parameter discovery tool, the description is complete: it fully explains what assets are returned and that they are usable by name. No output schema or annotations are needed, and the description provides 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. The description compensates by clarifying what will be listed, which is the only relevant semantic context.

  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 (loadable assets: H3 models, images, videos, audio in the input folder). It also distinguishes itself from sibling tools by focusing on asset inventory rather than job management or generation.

  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 clearly establishes when to use the tool (when you need to know what ComfyUI can load). It does not explicitly mention alternatives, but sibling tools are obviously job-oriented, so context is sufficient without explicit exclusions.

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