parts_board

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

With no annotations provided, the description fully discloses behavioral traits: it is async, engine/resolution/prompt are server-fixed, returns an asset { id }, requires polling until taskStatus=2, output is accessible via files.image, and it costs 80 credits. This exceeds the typical transparency expected and leaves no hidden surprises.

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

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

The description is dense but every sentence contributes new information: purpose, input options, server-fixed constraints, return shape, polling workflow, downstream usage, cost reference, and precedence. It is front-loaded with the core purpose and efficiently packs all needed details without fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (async, multiple input modes, downstream integration, cost), the description covers all critical aspects: the full workflow from input to polling to output consumption, prerequisite conditions (ownership, public URL, size limit), and integration with sibling tools. No output schema exists, but the description adequately explains the return format and subsequent steps.

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

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

Schema coverage is 100% with descriptions for all three parameters, but the description adds value beyond the schema by explaining the precedence order (image_path > asset_id > image_url), the requirement that asset_id must be an IMAGE asset you own, image_url must be public http/https, and image_path allows direct upload with a size limit (≤20MB). This enriches the parameter semantics significantly.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description opens with a specific verb-resource pairing: "Decompose one reference image into an exploded 'parts board' image (async)" and clarifies the output as "the subject laid out as separated components on one canvas." It distinguishes itself from siblings like generate_image and generate_3d_from_image by specifying it produces a parts board and explicitly directs feeding the output to generate_3d_from_image.

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

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

Provides clear usage context: explains the async workflow (call wait_for_asset/poll get_asset until taskStatus=2), what to do with the result (feed to generate_3d_from_image), cost (80 credits), and where to find model details (list_models(category='parts-board')). It also states input precedence (image_path > asset_id > image_url), which is actionable guidance beyond basic alternatives.

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