generate_prd

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'using AI analysis and industry best practices,' which hints at generative behavior and external data usage, but doesn't specify details like output format, potential rate limits, authentication needs, or whether the generation is deterministic. For a tool with 9 parameters and no annotations, this leaves significant gaps in understanding how it behaves.

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 a single, well-structured sentence that efficiently conveys the core purpose without unnecessary words. It's front-loaded with the main action and includes key details like 'comprehensive,' 'AI analysis,' and 'industry best practices,' making it easy to parse. Every part of the sentence earns its place.

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?

Given the complexity (9 parameters, 5 required, no output schema, and no annotations), the description is incomplete. It doesn't explain parameters, output format, or behavioral traits, leaving the agent with insufficient information to use the tool effectively. The lack of annotations and output schema increases the burden on the description, which it doesn't meet.

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 0%, meaning none of the 9 parameters have descriptions in the schema. The tool description does not mention any parameters, failing to add meaning beyond the schema. Parameters like 'projectIdea', 'targetUsers', and 'includeResearch' are left unexplained, making it hard for an agent to understand what inputs are expected. This is inadequate given the low schema coverage.

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's purpose: 'Generate a comprehensive Product Requirements Document (PRD) from a project idea using AI analysis and industry best practices.' It specifies the verb ('Generate'), resource ('Product Requirements Document'), and method ('using AI analysis and industry best practices'), which is specific and actionable. However, it doesn't explicitly distinguish from sibling tools like 'enhance_prd' or 'parse_prd', which might handle similar PRD-related tasks, so it's not a perfect 5.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'enhance_prd' (which might modify an existing PRD) or 'parse_prd' (which might analyze a PRD), nor does it specify prerequisites or contexts for usage. The agent must infer usage from the purpose alone, which is insufficient for optimal tool selection.

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