Compute

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

With no annotations provided, the description carries the behavioral disclosure burden and does well: it reveals defaults (secondary_series_id defaults to cpi_headline, population_series_id defaults to population_malaysia, base_period defaults to latest date in range), required conditions (index_to requires base_period), and a caveat about CPI being a consumer basket rather than a GDP deflator. It does not describe return formatting, but the presence of an output schema reduces the need for that.

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 well-structured as a scannable bulleted list with a clear opening phrase. Every line adds meaningful operational detail, and the caveats are woven in without padding or redundancy.

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?

Given the tool's complexity, eight parameters, no annotations, and an output schema that can describe returns, the description is largely complete. It covers operation semantics, defaults, requirements, and caveats. The main gap is that shared date-range behavior for date_from/date_to across operations is only implied rather than stated.

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 must compensate, and it largely does. It explains operation values, series_id as the primary series, secondary_series_id as a deflator or total, population_series_id for per_capita, base_period for index_to, and periods_ago for contribution_to_growth. However, date_from and date_to are only mentioned in the CAGR bullet and their general behavior across operations is not fully specified.

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 identifies a specific purpose: statistical operations beyond plain lookup, and enumerates discrete operations (yoy/mom/qoq, cagr, real, per_capita, index_to, contribution_to_growth). It distinguishes itself from siblings by positioning the tool as the uniform entry point for derived calculations, including explicitly noting overlap with get_series.

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 gives concrete usage context: 'beyond a plain lookup' and explicitly says yoy/mom/qoq matches get_series(transform=...), making this the alternative entry point. It also specifies when special parameters are required, such as base_period for index_to and secondary_series_id for contribution_to_growth. It does not broadly cover when not to use each sibling, but the guidance is clear for the main alternatives.

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