data_kuznets_extended

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

With no annotations, the description carries the behavioral burden. It discloses the return format (JSON array), frequency (annual), time coverage (1947 onward), source (FRED), and the set of included indicators, which is substantial for a read-only data retrieval tool. It does not mention caching, update cadence, or units, but those are secondary given zero parameters and an available output schema.

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?

A single compact statement packs the source, cycle name, extension status, start year, three constituent indicators, frequency, and output format without filler. Every segment contributes meaningful selection and invocation information.

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?

Since the tool takes no parameters and an output schema is already available, the essential information for selecting and invoking it is present: dataset identity, source, time range, constituents, frequency, and format. No invocation-critical detail 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?

The input schema is an empty object with no required or optional parameters, so there are no parameter semantics to explain; the 0-parameter baseline of 4 applies. The description's listed terms describe the returned data rather than input arguments, which is appropriate here.

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 concrete dataset: the Kuznets cycle FRED extended edition starting in 1947, covering US house prices, housing starts, and residential investment, delivered as an annual JSON array. It is distinguishable from sibling cycle tools by its 'extended' scope, FRED source, and listed series, but it is phrased as a noun phrase rather than an explicit verb like 'returns'.

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 no explicit guidance on when to use this tool versus data_kuznets, kuznets_cycle, chart_kuznets_cycle, data_kitchin_extended, or data_juglar_extended. The word 'extended' implies a relationship to a basic version, but there is no stated when-to-use or when-not-to-use context.

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