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

With annotations already marking the tool as read-only, open-world, and idempotent, the description adds substantial behavioral detail: the 150-most-recent cap, per-currency breakdown, annualization, USD-equivalent conversion, and the exclusion of estimates. This goes well beyond the annotation hints.

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?

The description is a single dense paragraph that front-loads the purpose and packs the output shape and limitations into compact clauses. There is no filler, and every phrase adds meaningful information.

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?

There is no output schema, but the description enumerates the returned statistics, currency handling, company ranking, and source limitations. Combined with the full parameter schema and annotations, an agent has sufficient detail to invoke the tool 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?

The input schema already documents all four parameters with 100% coverage, so the description does not need to repeat them. It lightly reinforces that the query is a role query but adds no new parameter-level semantics. Baseline 3 is appropriate.

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 clearly states what the tool computes: salary statistics (count, min, percentiles, median, max, top companies) for a role query from ENTRA listings. This is unambiguously distinct from sibling tools like search_jobs, list_companies, and match_jobs.

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 clear context: use it for role queries against ENTRA listings that publish salary bands, and it explicitly warns that it covers listed bands only, not estimates. It does not explicitly name alternatives or say when not to use it, but the intended use case is well implied.

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