generate_prompt_pack

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the intended action ('Generate...') without revealing side effects, whether it creates or updates resources, authentication needs, or the return value. The description is essentially the purpose restated, not an explanation of behavior.

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 extremely concise: a single lead sentence plus a three-line Args explanation. Every sentence adds value, with no filler or repetition of schema details. It is well-structured with the main action first, followed by the parameter clarification.

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 tool with a single simple parameter and no output schema, the description is minimally adequate. However, the lack of annotations means the description should disclose what 'generate' does operationally — e.g., does it create a new resource, modify the product idea, or return a generated pack? This missing context leaves it just at the 'adequate but clear gaps' level.

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 schema provides only the parameter name and type (string), but the description's Args block adds crucial meaning: the product_idea_id must belong to a product idea with product_type='prompt_pack'. This directly compensates for the 0% schema description coverage and fully clarifies the input.

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 action ('Generate') and the resource ('categorized AI prompt pack'), and explicitly scopes it to a product idea of type 'prompt_pack'. This immediately distinguishes it from sibling generation tools like generate_ebook, generate_checklist, and generate_template.

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 and parameter explanation specify the exact precondition: the tool is for product ideas with product_type='prompt_pack'. This gives clear usage context. It does not explicitly name alternatives or say 'use generate_ebook for other types', but the constraint is strong enough to guide selection.

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