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

No annotations are provided, so the description carries the full disclosure burden. It reveals a meaningful behavioral trait — that the output is questions to be answered independently rather than facts to be trusted — but omits whether this is a read-only operation, latency/cost, or any side effects.

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?

Two tightly written sentences with no filler. The core purpose is front-loaded and the follow-up instruction adds actionable value.

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?

With no output schema present, the description must explain the return, and it does say the tool returns the questions a buyer asks. It is nearly complete for a 4-param tool, though it could note that depth controls how many questions come back.

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%, so the schema already documents site, depth, offer, and company. The description adds no parameter detail beyond restating the general theme of questions about a company, so baseline 3 applies.

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?

States a specific verb and resource: returns the questions a buyer asks about a company, framed as an 'AI facts check'. It hints at its position relative to reading company material but does not name either sibling (company_statements, verify_findings), so the differentiation is only implicit.

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?

Gives a clear workflow instruction: answer the returned questions yourself using web search before reading anything from the company. This establishes ordering versus company_statements, but it never names the alternative tools or states when NOT to use this one.

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