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asset_enhance_prompt

Read-onlyIdempotent

Classifies an asset brief, routes it to the correct image model, rewrites the prompt in the model's dialect, and reports available execution modes with clarifying questions for ambiguous briefs.

Instructions

Classify an asset brief, route to the right model, rewrite the prompt in that model's dialect, and report which execution modes are available (inline_svg / external_prompt_only / api). Returns an AssetSpec JSON including modes_available, optional svg_brief (for inline_svg), optional paste_targets (for external_prompt_only), and — when the brief leaves a material ambiguity — a clarifying_questions[] array the host LLM should surface via AskUserQuestion (or the equivalent) BEFORE calling a generator. Each entry has {id, header, question, options[], required, why}. Read-only; idempotent; no network.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
briefYesPlain-English description of the desired asset
vectorNo
asset_typeNo
transparentNo
brand_bundleNo
target_modelNoForce a specific model; otherwise selected by router
text_contentNoLiteral text to render in the asset
Behavior4/5

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

Adds value beyond annotations by describing the clarifying questions array and modes reporting behavior. Annotations already indicate read-only and idempotent, and description reinforces that consistently.

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 dense and front-loaded with the main purpose, but it packs multiple aspects into one sentence. Could benefit from bullet points for clarity, but no wasted words.

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?

Output is well-described (modes_available, clarifying_questions), but several input parameters lack explanation in both schema and description. No output schema exists, so the description partially compensates but is incomplete regarding input semantics.

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?

Only adds meaning for 'brief' (plain-English) and implicitly for 'target_model' (force model). With 43% schema coverage, the description does not adequately explain parameters like vector, asset_type, brand_bundle, etc.

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 classifies, routes, rewrites, and reports execution modes, distinguishing it from sibling generation tools by being a preprocessing step that returns an AssetSpec rather than an actual asset.

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 advises surfacing clarifying questions before calling a generator, and notes the tool is read-only and idempotent. However, it does not explicitly state when to use it over siblings like asset_capabilities.

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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