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explain_prompt

Breaks down image prompts into individual tags, categorizes each tag, and explains its effect on generation. Also provides a canonically ordered version for consistent prompt structure.

Instructions

Explain what each tag in a prompt does.

Breaks down the prompt into individual tags with:

  • Category classification (quality, composition, character, etc.)

  • Explanation of what each tag affects

  • Canonically ordered version of the prompt

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesThe prompt to explain

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations provided, the description carries the full burden. It transparently discloses the tool's behavior: breaking the prompt into tags, classifying them, explaining their effects, and producing a canonical ordering. This gives the agent a clear picture of what happens, though it doesn't explicitly state that it's a safe, read-only operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured: a one-sentence purpose statement followed by a tight bullet list of output components. Every line adds value without redundancy or excess length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (one parameter, no annotations, output schema exists), the description covers all necessary aspects: the input prompt, the breakdown process, and the key output elements (classification, explanation, canonical order). The presence of an output schema relieves the description from specifying return format, so this is complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter 'prompt' is fully described in the schema with 'The prompt to explain' (100% coverage). The description adds context about how the prompt is processed (broken into tags) but does not add new format or constraint details beyond the schema, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Explain') and identifies the resource ('each tag in a prompt'), then elaborates with concrete deliverables (category classification, explanation, canonical ordering). This clearly distinguishes it from siblings like validate_prompt and optimize_prompt.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for understanding prompt tags but does not explicitly state when to use this tool versus alternatives like validate_prompt or optimize_prompt. No exclusions or alternative recommendations are provided.

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

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