recommend_deployment

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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds useful context beyond that: the operation costs '$0.25 USDC', and the recommendation is deliberately bounded to 'StackBench fit, performance, confidence, evidence, caveats, and economics only when supported', suggesting conservative behavior. There is no contradiction with 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?

The description is a single compact sentence with no filler, and the fee is mentioned up front. However, it is dense and relies on unexplained domain jargon such as 'bounded StackBench' and 'only when supported', which slightly undercuts its structural clarity.

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 42-parameter tool with 0% parameter coverage, this description is far from complete. Annotations and output schema help, but the description still does not tell an agent which parameters are central, what 'recommend what to buy or run' produces, or how this differs from sibling tools. It is a minimal selection-level description, not an invocation-level one.

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 0% across 42 parameters, and the description does not compensate. It neither explains the required model and prompt_tokens inputs nor any of the many mode, policy, threshold, or constraint parameters. The conceptual list in the description does not map to any specific schema property, so an agent gets almost no parameter-level meaning beyond names and enums.

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 uses a specific verb and resource: 'recommend what to buy or run', and it lists the main inputs the recommendation is based on (fit, performance, confidence, evidence, caveats, economics). It is reasonably distinct from siblings, but the phrase 'from bounded StackBench... only when supported' is jargon-heavy and does not explicitly differentiate it from generate_launch_config or predict_performance.

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 guidance is given about when to use this tool instead of check_deployment_fit, predict_performance, search_evidence, or generate_launch_config. The description implies a synthesizing recommendation role, but it never states conditions, exclusions, or alternatives, so an agent has to infer the decision boundary.

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