GDP growth (quarterly)

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), and the description adds substantial domain context: base year 1400 = 2021, that growth_pct is a constant-price change on the same quarter a year earlier, what gdp_ex_oil_pct excludes, which activities sit under 'industry', and that levels are in billion rials. With no output schema, these field definitions and the 'latest first' ordering are exactly what the agent needs to interpret results.

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 core purpose is front-loaded in the first sentence, with definitions and the am_table escape hatch following. It is dense and mostly earns its length, though the parenthetical listing of industry sub-sectors is slightly padded.

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 read tool with no output schema, the description supplies the return-value semantics (measure definitions, units, sector coverage, ordering) and the fallback route for deeper data. Nothing an agent needs to call or interpret this tool is missing.

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 both parameters are already documented in structured data. The description reinforces the meaning of 'latest first' and the quarter-vs-year distinction but adds no format or constraint detail beyond the schema, so the baseline applies.

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 ('Get Iran's official GDP growth by quarter or year') plus the content returned ('main sectors, latest first'). This clearly separates it from siblings like am_inflation, am_labour_force, and am_table, which cover different datasets.

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?

Explicitly routes the agent elsewhere for detailed breakdowns: 'For the full tables (value added by activity, expenditure side) use am_table with the `source.release` id.' That is a concrete alternative with a condition, though it does not cover other plausible near-neighbours such as am_indicators.

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