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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower, yet the description adds real behavior: the verdict grading scheme, the answer categories, and that later_cuts_not_ai_linked are deliberately never counted. It also discloses the not-found response, which is exactly the kind of edge-case behavior an agent needs.

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?

Key question is front-loaded and the dense clauses each carry return semantics rather than filler, which is justified because there is no output schema. It is one long run-on paragraph with some redundancy ('answered either way') that bullets could tighten.

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, the description carries the burden of describing the response and does so thoroughly: verdict grades, answer categories, roles disclosed vs AI-attributed, per-event employer words and sources, and excluded later cuts. The not-in-register behavior is also covered, so an agent knows what it will and will not get.

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 100% and the schema already documents the single 'company' parameter including partial-name support and example values. The description's 'Accepts a company name such as Klarna or Salesforce' largely restates the schema, adding little beyond it.

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 register's verdict for one named company — and the company-scoped framing cleanly separates it from the siblings (count_ai_job_losses, get_ai_layoffs_index, search_ai_layoff_events). An agent can tell what it does and that it is the per-company lookup without opening any schema.

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 leading question ('Did a specific company cut jobs because of AI?') plus 'Accepts a company name such as Klarna or Salesforce' gives clear context for when to reach for this tool, and the not-in-register fallback is spelled out. It never names the alternatives (index, search, count) or states when not to use it, so it stops short of explicit routing.

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