Server Quality Checklist

Tool Scores

• render_previewA
  3.8/5.0
  Behavior4/5

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

  With no annotations, the description carries the full burden. It discloses offscreen rendering, no engine requirement, a pyrender dependency, and the allowed views values/default. While it doesn't mention side effects or return format, an output schema exists, so those omissions are less critical.

  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: the first states the core purpose, and the second packs parameter semantics and a dependency note. No fluff, well front-loaded.

  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 simple render tool with an output schema and a clear parameter schema, the description covers purpose, dependency, and views options. It doesn't elaborate on return behavior (handled by output schema) or pipeline timing, but overall it is adequately 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 0%, but the description adds meaning to the 'views' parameter by listing the allowed values (iso/front/back/top/side) and default ([iso]). It does not explain 'size' or 'glb_path' beyond what the schema already provides, so compensation is partial.

  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 performs 'Offscreen renders of a GLB', specifying the verb 'renders' and the resource type 'GLB'. This distinguishes it from sibling tools like generate_model or server_status by the render-specific action.

  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?

  There is no explicit 'when to use' or 'when not to use' guidance. The phrase 'no engine needed' hints at lighter usage compared to a full engine, but it does not mention alternatives or pipeline stage. The dependency note is operational, not usage context.

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

• server_statusA
  4/5.0
  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 checks report exact fixes and that queue depth and last job are included, which is useful. However, it does not explicitly state whether the operation is read-only, requires specific permissions, or has side effects, which is a gap for a tool with no annotation safety profile.

  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, front-loaded with the tool's role, followed by a concise list of checks and reports. Every word earns its place without unnecessary elaboration.

  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 zero-parameter diagnostic tool with an output schema, the description covers the main functionality and clearly states what will be checked and reported. It does not need to describe return values due to the output schema. A minor gap is the lack of explicit read-only behavior, but it is largely implied.

  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 input schema is empty. The description adds context about what is validated and reported, which is relevant but not parameter-level detail. With zero parameters, the baseline is 4 per the rubric.

  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 identifies the tool as a health check and first-run diagnostic, listing specific validation targets (binary, metallib, weights, weight layout, worker venv) and additional reporting (queue depth, last job). This distinguishes it from sibling tools like generate_model or render_preview, which focus on generation/rendering.

  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 for setup validation and diagnostics but does not explicitly state when to prefer this over siblings or provide exclusions. It mentions 'first-run diagnostic' which suggests a specific use case, but no clear when-not-to-use guidance is included.

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

• prepare_conceptA
  4.1/5.0
  Behavior4/5

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

  With no annotations provided, the description carries the burden of disclosing behavior. It does so effectively by stating refusal conditions ('Refuses inputs whose corners disagree (busy background)') and warning behavior ('Warns when the opaque fraction looks like a bad key'). These are meaningful beyond what the schema reveals.

  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 sentences, front-loaded with the core action, and every sentence adds unique value: the operation, the relationship to generate_model, and two behavioral constraints. No wasted words.

  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 description covers purpose, usage context, behavioral edge cases, and output format. An output schema exists, so return details are not needed. The only gap is the lack of parameter-level explanation, but overall the description is sufficiently complete for a moderately complex tool.

  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 description coverage is 0%, so the description must compensate by explaining the parameters. It mentions the concept image as input and RGBA PNG as output, but it never maps these to the actual parameter names (image_path, output_path) or clarifies output_path's null default behavior. The parameter semantics are under-specified.

  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 opens with a specific verb-resource statement: 'Key a plain-background concept image out to a centered square RGBA PNG.' It also explicitly distinguishes itself from a sibling tool ('Standalone version of generate_model's auto_cutout'), making its unique role clear.

  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 names the alternative (generate_model) and specifies the intended use case ('for callers that want the intermediate'). It implies when this tool should be chosen over the sibling but does not explicitly say when NOT to use it in favor of other alternatives, so it stops short of full guidance.

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

• finish_modelA
  4.4/5.0
  Behavior4/5

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

  With no annotations, the description carries the full behavioral burden. It explicitly discloses that geometry is untouched, that the tool alters albedo and creates an emissive texture, and that it is designed for re-runs. This is strong, though it does not clarify whether the input GLB is overwritten or how output_path influences file handling.

  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 compact and front-loaded, with four sentences covering the primary action, texture mechanisms, pipeline separation, and a key parameter. Every sentence adds functional value, and there is no redundant or filler content.

  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 8 parameters and no annotations, the description provides a solid overview of the tool's behavior, safety, and re-runnability. It stops short of specifying output_path semantics and explicit sibling-tool alternatives, but the presence of an output schema and the sibling names help fill remaining context.

  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 coverage is 0%, so the description must compensate. It explains the purpose of key params: tone_gamma/contrast/saturation for albedo tuning, accent_emissive for saturated accents, and seam_pinstripes with a 0-1 range and disable behavior. It does not explain seam_halo or explicitly define glb_path/output_path, but those are partially inferable from names.

  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 delivers a specific verb and resource: 'Apply the game-look texture pass to a generated GLB.' It clearly distinguishes the tool from siblings like generate_model by noting it is 'Separated from generation' and re-runnable without regenerating, making the tool's role in the pipeline unmistakable.

  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 establishes when to use the tool: after a GLB has been generated, and notes it can be reused with new tuning knobs. It implies this is the texturing step rather than generation/rendering, but it does not explicitly name sibling tools as alternatives or list exclusion cases.

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

• generate_modelA
  4.8/5.0
  Behavior5/5

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

  With no annotations provided, the description fully carries the behavioral disclosure burden. It reveals timing differences (~20s vs 3-4 min), the effect of auto_cutout on transparency, the meaning of finish=True, and the blocking queue behavior. This is exceptionally transparent for an AI agent.

  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 tightly packed into a few sentences with no filler. It front-loads the core purpose, then concisely explains options and side effects. Every sentence adds value, and the structure is logical (purpose → options → behavior).

  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 complexity (7 params, output schema exists), the description covers the main workflow flags, timing, queue behavior, and references finish_model. It does not mention prepare_concept as a prerequisite or explain output_path, but these are minor gaps. The presence of an output schema means return value details are not required, so the description is largely complete.

  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 coverage is 0%, so the description must add meaning. It does for paint, auto_cutout, finish, and texture_size (including allowed values 512|1024|2048). It omits seed and output_path, but these are self-explanatory from their names and defaults. The description compensates for most key parameters, though not all.

  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 opens with a specific verb+resource: 'Turn a concept image into a textured 3D model (GLB).' It clearly distinguishes this tool from siblings by focusing on generation, while finish_model is referenced as a separate follow-up step. The GLB format specification adds precision.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines5/5

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

  The description provides explicit usage contexts: paint=False for shape-only, finish=True for game-look pass with a pointer to finish_model, and auto_cutout for background removal. It also mentions the single-job queue, which tells the agent when to expect blocking. These are clear when/when-not scenarios.

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