historical_salary

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

With no annotations, the description carries the burden of behavioral disclosure. It provides useful behavioral details: returned keys are 'YYYY-MM' strings and 'order is not guaranteed by the API.' It also clarifies parameter semantics (e.g., country codes, hierarchical location paths). It does not mention rate limits, error behavior, or currency, but for a read-only query tool the disclosed information is substantive.

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 concise and well-structured: a one-sentence purpose, followed by the return-key behavior, then a compact parameter list. Every sentence adds value, and the most important information is front-loaded.

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 4-parameter tool with no output schema and no annotations, the description covers the essential aspects: purpose, parameter formats, return-key format, and how to discover valid values via regional_data. However, it does not describe the value type/currency of the salaries or any error/missing-data behavior, which would make it fully complete.

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 0%, so the description fully compensates by explaining each parameter with types, examples, and source references: 'country: ISO country code, e.g. "gb", "us", "de"', 'category: a category tag as returned by list_categories', 'months: how many months of history to return', and 'location: a hierarchical place, most general first, e.g. ["UK", "London"]'. This adds significant meaning beyond the raw schema.

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 opens with a specific verb and resource: 'Get average salary by month for a category and/or location, over time.' It clearly defines the tool's scope and differentiates it from siblings like salary_histogram (which likely provides a distribution, not time series) and regional_data (which is for discovering regions, not salary history).

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 description provides clear context on what the tool does and how filters work, and it references sibling tools: 'category: a category tag as returned by list_categories' and 'See regional_data for how to discover valid values.' It does not explicitly state when NOT to use this tool versus salary_histogram or other siblings, but the purpose is clear enough to infer appropriate usage.

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