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

Annotations already declare readOnly, idempotent, closed-world, non-destructive, so the safety profile is covered. The description adds genuinely non-obvious behavior: two distinct datasets (live Watch List vs. frozen October 2026 report) each carrying its own as-of date, plus a staleness/citation obligation — the kind of context an agent cannot get from structured fields.

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 tight sentences with zero filler. The live figures are front-loaded, the frozen dataset follows, and the citation rule closes as an actionable imperative.

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 return payload and does so by enumerating the figures, the two datasets, and the as-of dates. It stops short of describing shape or field naming, but an agent has enough to call it and interpret results correctly.

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?

Zero parameters and 100% schema coverage, so nothing needs documenting; the baseline of 4 applies. The description correctly does not waste words on inputs and instead spends its budget on return content.

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 live headline Clock figures') and enumerates exactly what the payload contains: public company count on the Clock, counts open more than one and three years, and the full on-record count. The only gap is that it never names or contrasts the sibling lookup_company, though the aggregate-vs-per-company distinction is obvious from the content.

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?

There is an actionable directive ('Always quote a figure with its as-of date') and an implicit split between the live dataset and the frozen October 2026 report. However, it never states when to call this versus lookup_company, nor any preconditions or exclusions — usage is inferred rather than specified.

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