real_savings_rate

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

With no annotations provided, the description carries the full burden. It transparently discloses the data sources (RBA + ABS), the computation (subtracting CPI), and the output fields, including the saver-stance flag. While it omits potential caveats like data frequency or revision policies, the description is substantially transparent for a zero-parameter data retrieval tool.

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 three sentences long, front-loaded with the primary purpose and formula, then a use-case question, and finally output details. Every sentence adds value with no filler or redundancy, achieving high information density in a compact form.

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?

In the absence of an output schema, the description fully explains the return values: nominal savings rate, CPI annual change, computed real rate, and flag. It also mentions the cross-sourcing of data, making the tool self-sufficient and complete for a zero-parameter function with no structured output metadata.

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 has zero parameters, so the baseline is 4 per the rubric. The description goes beyond the schema by explaining what the tool returns and how the calculation is performed, but since there are no parameters, there is little parameter-specific meaning to add. The baseline 4 is appropriate.

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 states the tool's function: 'Returns the current real (inflation-adjusted) bank term-deposit / savings rate' and specifies the exact formula (RBA retail deposit rate minus ABS CPI annual change). It distinguishes itself from siblings like real_cash_rate and real_wages by explicitly focusing on the savings/deposit rate.

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 provides a clear use case: 'Single call answers "are savers being compensated for inflation?"' This gives strong contextual guidance on when to use the tool. However, it does not explicitly mention alternatives or when not to use it, which would merit a 5.

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