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

No annotations are supplied, so the description carries the disclosure burden. It does add real behavioral detail beyond the schema: the metric is p50 and the series is monthly. However, it omits how far back the data goes, what happens when a role/city has sparse history, and any auth or rate-limit behavior.

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?

Three short sentences with the operation front-loaded and the metric detail immediately after. No filler or hedging, though the final usage sentence is close to redundant with the first.

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 simple two-parameter read tool this is close to adequate, and the description partially compensates for the absent output schema by naming the returned field (p50) and cadence (monthly). It leaves unclear how much history is returned and whether the caller can bound the time window at all, which matters for a time-series tool.

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%, so the schema already documents both parameters. The description adds only the framing 'role/city combination' and does not clarify expected role format or whether city naming must match list_cities, which would have compensated for the terse schema 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?

States a specific verb and resource ('Retrieve historical salary trend data for a role/city combination') and pins down the exact metric and granularity ('p50 salaries have changed month over month'). This distinguishes it from aggregate siblings like get_market_summary or get_stats, though no sibling is named 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 sentence 'Use for salary inflation analysis and time-series compensation modeling' gives intended use cases, but it is essentially a restatement of what the tool returns. There is no guidance on when to prefer it over get_market_summary or get_dataset_trends, and no exclusions or prerequisites.

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