queue_genbatch

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

With no annotations, the description carries the burden and does substantial work: it discloses that this is an enqueue (not a synchronous render), that a separate seat consumes the queue, that a measured cost model prices the ask as p50/p95, and that outputs get provenance and licence metadata. It doesn't state whether queued jobs can be cancelled or what happens on rejection, keeping it below 5.

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?

Three sentences, front-loaded with the verb and resource, but the prose is dense and metaphorical ('a night of props', 'ride the intention', 'the genbatch seat consumes the queue'), which costs parsing effort without adding precision.

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 5-parameter mutation-ish tool with no annotations and no output schema, the description conveys the async queue semantics and pricing behavior well but omits what is returned on enqueue and leaves two parameters (night, cost_estimate) undocumented anywhere. Adequate, not complete.

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 0% across 5 parameters, so the description must compensate and partially does: it explains kind (prop.512/prop.1024, priced per variant), count (scales p50 ask and p95 rail), and intention (what to make, how many seeds). However 'night' and 'cost_estimate' receive no explanation at all, leaving real gaps.

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+resource ('Queue a generative batch (genbatch)') and describes the full pipeline: local image model + TRELLIS.2 producing measured, judged props recorded as assets with provenance and licence. Jargon ('a night of props') slightly obscures the object, but the operation is distinguishable from siblings like request_render or propose_plan_intention.

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?

No explicit when-to-use, when-not-to-use, or named alternative is given. The phrase 'the genbatch seat consumes the queue' hints at the async execution model but doesn't tell an agent when to pick this over request_render or propose_plan_intention. Usage must be inferred.

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