nuclear_prompt_breakdown

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

No annotations provided; description mentions it works without external LLM calls but does not disclose other behavioral traits (e.g., data handling, side effects, rate limits, or auth requirements). Minimal transparency beyond the core action.

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?

Two sentences, zero wasted words. Front-loaded with action, lists outputs clearly. Efficient and to the point.

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?

Covers the tool's complexity by listing all extracted components and the offline nature. Since output schema exists, return values are covered. Lacks prerequisites or context for when not to use, but overall sufficient.

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?

With 0% schema description coverage, the description compensates by explaining that the single parameter 'prompt' is the text to decompose into the listed components. Adds meaning beyond the schema, though no format or constraints are detailed.

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?

Uses specific verb 'decompose' and resource 'prompt', listing multiple outputs (explicit/implicit requirements, constraints, etc.). Clearly differentiates from sibling tools like 'polish_prompt' or 'analyze' by its comprehensive extraction focus.

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?

Implies usage for analyzing prompts, but does not specify when to use vs alternatives. Mentions 'works without external LLM calls' as a characteristic, but lacks explicit when-not or alternative tool references.

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