get_rolling_returns

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

Without annotations, the description details key behavioral outputs, specifying that it returns min, max, mean, median, and frequency counts. However, it lacks crucial operational details, such as how many historical windows are generated or if the data is guaranteed to be current.

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 definition is highly structured and flows logically: first defining the resource, then listing the contents of the result, and finally providing the unique value proposition. It is concise without being overly simplistic.

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?

The description is complete enough to define the value and structure of the output metrics (min, max, mean, median). However, it falls short on technical completeness by not clarifying how the input parameters like 'step days' affect the calculation granularity.

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?

Given 0% schema coverage, the description is required to clarify parameter roles beyond the schema. It mentions `window_years` and `threshold_pct`, but fails to provide semantics or context for the remaining technical parameters, `step_days` and `scheme_code`.

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 defines the purpose as generating a 'Rolling-return distribution for a fund.' It distinguishes this from standard returns by stating it is 'Better than a single start-to-end return,' effectively differentiating it from calculation siblings.

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 provides strong conceptual usage guidance by comparing the tool's output to 'a single start-to-end return.' This establishes a clear context for when the agent should select it over simpler historical return functions.

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