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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world, so the safety bar is low. The description adds real value beyond them by disclosing that prices are workspace-specific rates in credits and that returned inputs describe how each model must be invoked. It omits listing behavior (ordering, volume) but nothing contradicts the annotations.

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?

Roughly three sentences, front-loaded with what is listed and what each entry contains, then routing guidance. Every clause carries information, though the trailing 'where that tool is listed' is slightly awkward and the parenthetical example is dense.

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?

There is no output schema, so the description correctly compensates by naming the return fields (ids, inputs, per-unit price). For a zero-parameter discovery tool with full annotation coverage, the agent has everything needed to call it and route the result; only ordering/volume behavior is unspecified.

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?

The tool takes zero parameters, so there is nothing for the description to disambiguate at the schema level and the baseline applies. The description instead spends its words on how to consume the output ids, which is the relevant semantic guidance here.

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 ('Lists the generation models by public id') and adds what each entry carries: inputs and per-unit credit price at this workspace's rate. It also clarifies the app_name vs id distinction, so an agent knows exactly what each row represents before calling.

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 an explicit workflow (match a person's spoken model name against app_name, then pass the id) and names the alternatives: use start_run with the model to execute, or chat_completion for text/auto modality models. Both when-to-use and when-to-use-something-else are covered.

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