interpret_prompt

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

With no annotations provided, the description carries the full behavioral disclosure burden. It states the tool does not generate anything, which clarifies that the tool is read-only in intent, and it describes what will be returned (mood, industry, colors, sections). This covers the most important behavioral traits, though it does not discuss potential limitations or error cases.

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 three sentences, front-loads the core action, and wastes no words. Every sentence contributes either a behavioral guarantee, an output summary, or a use case.

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 single-parameter tool with no output schema, the description is complete: it says what the tool does, what it returns, and when to use it. The absence of an output schema is mitigated by the explicit list of interpretation fields (mood, industry, colors, sections).

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%, and the input schema already describes 'prompt' as 'Design description to analyze.' The tool description adds context about what the analysis yields, but it does not materially improve the meaning of the parameter itself beyond the schema.

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 states a specific verb ('analyzes a design prompt') and a clear resource, and explicitly says it 'returns the interpretation without generating anything,' which distinguishes it from generation-oriented siblings like design_from_prompt. It names the kind of output (mood, industry, colors, sections), making its purpose 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 phrase 'Good for previewing before generating' gives a clear context in which to use this tool, implying it is the right choice when a non-destructive preview of prompt interpretation is needed. It does not explicitly name an alternative, but the sibling set makes the generation tool obvious.

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