Gpu Infer

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

Annotations only declare the safety profile (not readOnly, openWorld, non-idempotent, non-destructive). The description goes well beyond that: provider selection by quota, fallback on rate limit/outage, spend counting, cooldown waiting, the auth_required failure path with the exact login command, and an explicit prompt-injection warning that `text` is untrusted data. This is exactly the behavioral context annotations cannot carry.

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?

A dense but front-loaded paragraph where nearly every sentence adds a fact (providers, routing, inputs, dry_run, return shape, safety). It is longer than most, but the only mild redundancy is restating the return shape that the output schema already covers.

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 tool with an output schema, the description covers inputs, routing, fallback, quota, failure modes, and return fields. Nothing an agent needs to call it correctly or interpret the result is missing.

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 89%, so the baseline is 3, but the description adds real meaning: the prompt/system-vs-messages mutual exclusivity, alias-style model values, and the provider-pinning option. It stops short of explaining wait_s semantics beyond what the schema already states.

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 ('One LLM chat completion on a free inference API') and immediately disambiguates the biggest risk: 'NOT a GPU job'. It names the actual providers and contrasts with the GPU-job siblings, so an agent can tell it apart from gpu_submit without opening any schema.

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?

Gives concrete use cases ('eval prompts, judging outputs or quick generations with a hosted model') and the negative case ('NOT a GPU job'). It doesn't name the sibling to use instead for GPU work, but the routing condition and the dry_run escape hatch are both spelled out, leaving little to inference.

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