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

Annotations already cover read-only, idempotent, non-destructive behavior. The description adds valuable behavioral context beyond that: it returns year-over-year plus 1/3/6-month annualized readings, identifies basket drivers, and notes that published statistics answer on every data tier. No contradictions with the annotations.

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 dense but each sentence contributes: it enumerates scope, explains return calculations, mentions basket-driver attribution, and maps the key parameters. It is a bit lengthy, but the front-loaded scope line helps an agent quickly recognize relevance.

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 there is no output schema, the description does a good job explaining what will come back: measure list, YoY plus annualized horizons, and driver attribution. It could be more explicit about response shape and supported measure keys, but the tool has zero required parameters, so an agent can successfully call it with no arguments.

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 100%, so the baseline is 3. The description restates the `measures` filter and `history` series behavior but does not meaningfully extend what the schema already provides; it gives no additional detail about `window` or valid measure keys beyond the schema examples.

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 identifies the specific resource: all US inflation measures, with an explicit enumeration of measure families (CPI, PCE, trimmed means, median, sticky/flexible, CPI components, market expectations). This clearly distinguishes it from the sibling market/rates tools, and the return behavior is implied with 'Each one comes back with...'.

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?

It clearly implies when to use the tool: whenever an agent needs US inflation data across many measures, especially when annualized rates or basket-driver attribution would otherwise require manual calculation. It does not explicitly name alternatives or state when not to use it, but the context is strong.

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