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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds useful context with 'live hiring-market snapshot', signalling freshness, but says nothing about caching, coverage guarantees, or the shape of the facet output beyond naming the dimensions.

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?

One dense sentence, front-loaded with the verb and resource, then the facet dimensions, and closing with a parenthetical on data freshness. No filler or redundancy.

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 a one-parameter, read-only aggregate tool with an output schema and read-only annotations, the description covers what the tool produces. The remaining gap is routing information relative to the sibling jobs tools (search, companies, nearby), which would help an agent choose 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?

There is a single optional parameter ('size') with 100% schema description coverage (buckets per facet, default 20, max 100), so the schema does the heavy lifting. The description adds no further parameter meaning, which is the expected baseline when schema coverage is complete.

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 ('Facet') plus the resource ('jobs dataset') and enumerates the facet dimensions produced: top companies hiring by provider/department/location/seniority/job family, plus remote share. This clearly distinguishes it from datasets_jobs_search, though it never names the sibling explicitly.

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 aggregation framing ('Facet the jobs dataset') implies this is for distribution/aggregate questions rather than retrieving individual postings, but there is no explicit when-to-use or when-not statement and no reference to datasets_jobs_search or datasets_jobs_companies as alternatives.

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