Optimize Prompt

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

With only the openWorldHint annotation (which doesn't clarify behavior), the description carries the burden of explaining what happens. It does not disclose that the tool returns the optimized prompt, nor any dependencies like the need for a target model. The behavior is under-specified; for instance, it's unclear whether the input prompt is modified in place or a new prompt is returned.

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 sentence, front-loaded with the action, and contains no fluff. It is concise, though it could be more informative without becoming verbose. It earns a strong score for efficiency.

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?

The tool has only two documented parameters and no output schema, so the description needs to cover the return value and usage context. It does not state that the output is the optimized prompt, nor does it explain how 'target_model' influences the optimization. It's adequate for a simple tool but leaves gaps.

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 100%: both 'prompt' and 'target_model' have descriptive text in the schema. The tool description adds no additional parameter semantics beyond the schema, so the baseline of 3 is appropriate.

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 clearly states the tool refines/optimizes a draft prompt using LLM best practices. The verb 'refine' and resource 'draft prompt' are specific and distinguish this from sibling tools like chat_completion. It doesn't detail what 'optimize' entails, but the core purpose is unambiguous.

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?

The description implies using the tool when you have a draft prompt to improve, but offers no explicit when-to-use or when-not-to-use guidance. No alternatives are suggested, and there are no exclusions. The context is clear enough for a simple tool, but the guidance remains implicit.

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