Auto-optimize prompt (score-driven)

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

With no annotations, the description carries the full burden and does well: it discloses the multi-round search loop, the three named strategies, the dev-set gate for keeping candidates, budget guarding against maxCalls, early stopping on no improvement or time limit, and even a concrete cost estimate ('~40-60 API calls' for 6 cases). This is unusually rich behavioral disclosure for a mutation/expensive operation.

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?

Front-loaded with the pipeline and dense with substance rather than filler; every clause (strategies, dev gate, budget guard, cost, return contents) earns its place. It is a single long block, which slightly hurts scannability, but it is appropriately sized for a tool of this complexity.

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 17-param tool with no output schema, the description usefully enumerates what is returned (best prompt, scoreboard, per-case before/after, cost, saved report) and the stopping/budget behavior. Minor gaps remain: where the report is saved, and the model/env dependencies (EVAL_MODEL, UPO_MAX_CALLS) that only appear in the schema.

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 only 53% across 17 params, so the description has to compensate, and it does for the key knobs: it explains what reflect/template/fewshot do (GEPA-like, MIPRO-like, BootstrapFewShot-like), how the test set is sourced, and how budget/maxCalls shape the search. It still leaves several params (seed, temperature, promptMode, testsFile, maxMinutes) to the schema, but adds real meaning beyond it.

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+scope: metric-driven prompt optimization that builds a test set, splits train/dev, proposes candidates, and keeps only dev-improving ones. This is clearly distinguishable from siblings like optimize_prompt_via_api or evaluate_prompt_preview 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?

It explains the internal conditions that drive behavior ('given or auto-generated' test set, budget scaling, early stop on no improvement), which is helpful context, but never states when to choose this over the sibling optimizer (auto_optimize_prompt vs optimize_prompt_via_api vs suggest_prompts). Usage is implied rather than routed.

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