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

Annotations already cover read-only/idempotent/open-world, and the description adds genuinely new behavioural facts: every measure comes with YoY plus 1/3/6-month annualised, and the response says which basket components drive the headline. It also notes the data is published statistics available on every tier, which is real context beyond the annotations. It stops short of describing size limits, freshness lag, or cost implications of `fresh` beyond what the schema says.

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?

Front-loaded with the resource and scope, and the second and third sentences carry substantive payload about the return shape. The phrasing is dense and slightly long-winded, but nearly every clause earns its place; no filler sentences.

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?

With no output schema, the description steps in usefully by explaining the return shape (YoY plus 1/3/6-month annualised, plus headline drivers), which is exactly the gap an output schema would otherwise fill. It does not cover how `window` interacts with `history`, series length limits, or what the response looks like when `measures` is omitted, so it is not 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 coverage is 100%, so all four parameters are already documented in the schema. The description restates `measures` as a key filter and `history` as adding the monthly series, which is confirmatory rather than additive. `window` and `fresh` (including the 5-credit cost) are left entirely to 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?

The first sentence names the specific resource (US inflation measures) and enumerates the families covered — headline/core CPI and PCE, supercore, trimmed means, median, sticky/flexible, components. That enumeration clearly separates it from siblings such as get_jobs, get_economic_calendar, or get_rates_and_curve without the agent opening any 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?

Usage is implied rather than stated: it is the tool for US inflation readings, and the description notes `measures` filters by key while `history` adds the monthly series. It does not say when to prefer this over e.g. get_series for a single CPI series, nor any exclusions, so the agent has to infer routing.

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