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

With no annotations, the description carries full behavioral disclosure. It reveals assumptions (goal/budget defaults), that unanswered fields become gaps rather than rejections, that it does not require an account, and that it suggests storing answers via a separate tool. This is transparent about its non‑mutating nature and the kind of output it produces, covering important behavioral expectations.

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 fairly long but each sentence adds meaningful content: purpose, output, ordering logic, assumptions, workflow, and account requirement. It is front‑loaded with the core purpose and logically structured, though a bit verbose for a tool description. It earns its length given the tool's complexity.

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?

The description explains the output categories and the recommended order, and the next steps (ask questions, call again, store). It does not detail the exact return structure or enumerate the criteria for each qualification tier, but given that there is no output schema and the tool's complex behavior, it provides enough for an agent to invoke it and process the result sensibly.

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 coverage is only 19% (goal, budget, categories, launchAt), and the main description does not define most of the 21 parameters. It does add global semantics: every field is optional and missing fields become gaps to fill. This helps, but it does not explain individual boolean/attribute fields (e.g., docs, demoVideo, coverImage) beyond being product attributes. The description compensates at a high level but not per‑parameter.

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 is explicit: it states the tool qualifies a product against platforms, distinguishing three outcomes (qualifies now, qualifies after filling gaps, structurally out of reach). It also names the `recommended` output and how it is derived, clearly differentiating from siblings like search_platforms and plan_submissions by focusing on qualification and ordering.

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 gives clear context: it is used inline without an account, takes product details, and every field is optional. It explains the workflow of asking questions, calling again, and storing answers via update_project. However, it does not explicitly name alternative tools or state when not to use it, though the overall flow implies its use case.

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