polish_prompt

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

With no annotations, the description provides substantial behavioral details: it fetches active goals, scores quality, adds checks, and scales requirements. However, it does not disclose side effects like storage or error conditions.

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 highly concise and well-structured, starting with a clear emoji-led statement, followed by bullet points explaining the polisher's actions, with no unnecessary words.

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 description adequately explains the tool's process and output, and given the existence of an output schema, return value details are unnecessary. It lacks edge cases or error handling, but overall is complete.

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 description adds minimal meaning to the single parameter 'user_prompt' by labeling it as 'raw user prompt to polish.' Schema coverage is 0%, so more detail on format or constraints would help, but the purpose is clear.

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 polishes prompts for better output quality, specifying actions like analyzing, aligning with goals, and adding directives. It distinguishes itself from siblings like analyze_prompt_sequence by focusing on enhancement, but lacks explicit differentiation.

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 provided on when to use this tool versus alternatives such as analyze or analyze_prompt_sequence. The description implies general use for prompt polishing but omits exclusions or context.

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