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

Annotations already declare readOnly, openWorld, idempotent and non-destructive, so the safety profile is covered; the description adds real value beyond that. It discloses data provenance (employer career pages), a material sampling caveat ("it is a large sample, not a count of every job"), the anonymity/threshold rule for search data ("anonymous, 10 or more searches"), the US-only WARN Act scope, and that a prewritten summary lives in `say`. It does not mention freshness/lag of the index or pagination, which would complete the picture.

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?

Purpose is front-loaded in the first clause, followed by example queries and then the metric inventory. The long middle enumeration of metrics is dense but earns its place because there is no output schema and it is the only signal of what comes back. Minor cost: the metric list reads as a run-on and could be trimmed or split.

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 returns, and it does so thoroughly — the metric inventory, the `say` summary pointer, the sample-size caveat, and the anonymization threshold. Combined with annotations covering the read-only profile and the schema covering all filters, an agent has everything needed to call it and to caveat the results.

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% and each of the three optional filters already carries its own description with examples and the function vocabulary. The description reinforces the dimensions ("a split by job area and country") but adds no new syntax, defaults, or combination rules beyond what the schema states, so the baseline of 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?

The description states a concrete subject (the live hiring market) and names the data source explicitly: "Job Spotter's own index of employers' career pages," then enumerates what it reports (open roles, week-over-week adds, takedowns, pay, AI share, WARN layoffs). That is far more specific than a restated title. It stops short of 5 because it never distinguishes itself from plausible siblings such as get_hiring_stats, whats_hiring or top_hiring_companies — an agent must infer the boundary from topic alone.

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?

Usage is anchored in quoted user intents ("how's the job market?", "is tech hiring picking up?", "is it a good time to look for sales jobs in the UK?") plus an explicit instruction: "Use it as fresh evidence when advising on where to look." That is clear context for selection. There is no explicit when-not or named alternative tool, so it falls short of the 5 bar.

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