Run any Luw.ai model (advanced)

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

Annotations cover the safety profile (readOnly=false, openWorld=true, idempotent=false, destructive=false), and the description adds value on top: it warns 'Spends credits like the model's own tool' and notes image fields accept local paths and data: URIs. It omits what the raw call returns, but the cost and input-handling disclosures are real additions.

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?

Front-loaded purpose, then scope condition, then the reference list. The long model enumeration earns its place as a discoverability aid for a generic pass-through tool; no filler sentences.

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 raw pass-through tool with no output schema, the description supplies the model list, the parameter docs link, and the credit-spend warning, which is enough to invoke correctly. It does not describe the response shape, a minor gap given no output schema exists.

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% (baseline 3), and the description goes further by listing accepted image field names, the parameter reference URL, and the full model id roster. This meaningfully reduces guesswork for the free-form 'params' object.

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 concrete verb+resource ('Call POST /generate with raw API parameters') and explicitly scopes itself as the escape hatch for cases the dedicated tools don't cover. Enumerating the supported model ids makes it immediately distinguishable from the ~24 specialized siblings.

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?

Clearly states when to reach for it: 'for models or options the dedicated tools don't cover (e.g. persona-slot inputs, webhooks)' — implying the specific siblings are the default. No explicit when-not, but the fallback framing is unambiguous.

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