Labour force

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

The annotations already declare this a safe, idempotent, open-world read, so the bar is lower; the description nonetheless adds real context the annotations cannot: the Statistical Centre survey as provenance, what each rate is measured against (active population vs population 15+), full geographic coverage, and a two-to-three-month publication lag. It does not describe the returned record shape, which matters because no output schema exists.

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 tight sentences, front-loaded with what/scope/ordering, then source and measurement definitions, then freshness and the alternative tool. Nothing is redundant and no sentence could be dropped without losing information an agent needs.

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 two-parameter, annotation-covered read tool with no output schema, the description supplies provenance, definitions, lag and routing to the right alternative. The remaining gap is the returned data shape and the undocumented 'youth_unemployment' option, both of which an agent must infer from the schema alone.

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 floor, but the description genuinely adds meaning by specifying the denominators behind each measure (percent of active population for unemployment, percent of population 15+ for participation) and by establishing the latest-first ordering that gives the 'quarters' count its direction. It never mentions the 'youth_unemployment' measure, leaving that enum value explained only by the 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?

Names a specific verb and resource ('Get Iran's official unemployment and labour-force participation rates by quarter'), states scope (whole country, urban, rural), ordering (latest first), and explicitly separates itself from am_search. An agent can distinguish it from am_inflation, am_gdp or am_population without opening the 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?

Gives both a positive condition (labour-force rates) and a negative routing rule: other labour tables such as employment by sector, hours or wages belong to am_search. It does not cover the remaining siblings (e.g. am_population or am_indicators) that might also look plausible to an agent, so it stops short of full when/when-not coverage.

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