macro_usa_real_consumer_spending

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so the safety profile is covered. The description adds genuinely new behavioral context: the data coverage window (2013-11-07 to present) and the return container (pandas.DataFrame), which the annotations do not express.

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 fact is front-loaded, but the entry is padded with a source URL and Sphinx docstring artifacts (:return:, :rtype:) rather than tool-call guidance. Nothing is confusing, yet a sentence or two of that space could have carried usage guidance instead.

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 zero-parameter, read-only data fetch with no output schema, the description supplies the essential missing pieces: subject, coverage window, and return type. Only cross-tool routing is absent, which is a minor gap at this complexity.

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?

The tool takes zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies. The description correctly implies a no-argument fetch, matching the empty 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?

States a specific resource (US real consumer spending quarterly preliminary report) and a concrete scope (data from 2013-11-07 onward), so an agent knows exactly what dataset comes back. It does not explicitly distinguish itself from the close sibling macro_usa_personal_spending, which is the main remaining gap.

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?

There is no statement of when to use this tool versus alternatives such as macro_usa_personal_spending or macro_usa_core_pce_price, and no prerequisite or freshness guidance. Usage is only inferable from the report name itself.

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