Text to image (Fluw AI)

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

Annotations already declare the non-read-only, non-idempotent, open-world nature, so the bar is lower. The description adds genuinely useful behavioral facts the annotations lack: a cost of 2 credits per image, per-generation billing for variations, and the engine switch triggered by svg. It still omits auth requirements, rate limits, and whether generation is synchronous or a job to poll via luw_get_result.

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?

Two tight sentences with the primary capability and inputs front-loaded, followed by the format rule and pricing. No filler, no repetition of the title.

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?

For a 9-parameter generation tool with no output schema, an agent still lacks the return contract — image URL vs. job identifier and any required follow-up (luw_get_result) — which is exactly the gap an output schema would normally close. Pricing, format, and engine selection are covered, so this is adequate but incomplete.

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 description coverage is 89%, so the schema already documents seed, image, engine, format, styles, and variations in detail. The description's only added semantics is the cost per image and per-variation billing, which the schema largely duplicates ('each is billed as a generation'). Baseline 3 is appropriate when structured fields carry the parameter detail.

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?

States a specific verb and resource ('generate a photorealistic image or illustration from a text prompt') plus input modalities (text prompt, optional guide image) and example domains. It is clearly the general-purpose text-to-image tool, but it never names or differentiates itself from specialists like luw_interior_design or luw_generate_pattern, so sibling routing is left to inference.

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?

Concrete use-case context is given ('concept art, interiors, products, marketing visuals') along with a format-selection rule (format='svg' for vector illustration). There are no explicit exclusions or alternative-tool pointers, and listing 'interiors' arguably overlaps with the luw_interior_design sibling without reconciling the two.

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