vLLM server job

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

Annotations declare readOnlyHint=false, openWorldHint=true, destructiveHint=false, which already conveys a mutating open-world operation. The description adds nothing about the critical dryRun=true/confirm=true submission gate — by default this tool builds and validates but does NOT submit, which the description's 'serve a model' phrasing actively obscures. It also omits resource/lifetime behavior (walltime, GPU allocation, what happens after the job ends).

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 single front-loaded sentence with the verb, resource, hardware scope, and return value; every clause earns its place with no filler.

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 14-parameter mutation tool with no output schema and low schema coverage, the description is far too thin. It does not explain the dryRun/confirm safety flow (the most decision-relevant fact), resource requirements, or the meaning of the many unlabeled parameters, leaving an agent to guess its way through invocation.

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 only 29% across 14 parameters, so the description is expected to compensate — and it mentions none of them. Only dryRun, confirm, profile, and project carry inline schema docs; cpus, port, dtype, memGb, model, account, condaEnv, gpuCount, walltime, and maxModelLen are undocumented, and the description does not clarify any of them.

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?

The description names a specific action (serve a model via vLLM), the resource (an OpenAI-compatible API), the hardware target (full-H100 node), and the return value (tunnel instructions). This clearly differentiates it from the many other ris_submit_* siblings, but it does not explicitly contrast itself against any of them.

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?

Usage is only implied: an agent can infer this is for hosting an inference server rather than a notebook, batch, or training job. There is no explicit when-to-use statement, no preconditions (e.g., model must be downloadable or present on the node), and no named alternative for related needs.

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