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

Beyond the readOnly/idempotent annotations, it discloses several non-obvious traits: results are a daily snapshot (not live), cells with fewer than 30 jobs are suppressed for privacy/statistical validity, and no account is needed. These are exactly the operational facts an agent cannot infer from annotations or schema.

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?

Four compact sentences with the capability statement front-loaded and constraints and the sibling redirect after it; almost nothing is wasted. The opening sentence is a long enumeration, but every item is a distinct supported metric rather than filler.

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?

For an 8-parameter, zero-required tool with no output schema, the description covers input rules, suppression, freshness and auth well. It says nothing about the shape of the returned figures, and the 'see as_of' reference points at a field that is never defined, leaving the agent guessing about response structure and how to read the snapshot date.

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%, so 3 is the baseline, but the description adds a cross-parameter constraint absent from the schema: at most two of title, place, company, seniority, employment_type and remote may be combined, and metric may be omitted to return everything. That is genuine meaning beyond the per-field descriptions.

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 names a specific verb and resource (look up job market figures) and enumerates exactly which figures: job count, pay, pay share, work arrangement, posting age, new postings, top employers. It also explicitly distinguishes itself from the sibling count_jobs for live counts, so an agent can choose without opening either 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?

It gives a concrete when-to-use rule ('up to two of' the six dimensions, omit metric for all figures) and one explicit alternative ('For live counts or who is hiring now, use count_jobs'). It does not route the agent between market_lookup and the closely overlapping market_breakdown, which shares nearly identical filters, so the guidance is clear but not exhaustive.

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