run_ts_validation

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

With no annotations, the description carries the full behavioral disclosure burden and does well: it reveals no look-ahead behavior, per-fold next-bar prediction, baseline comparisons, interval coverage computation, deterministic promotion, and result persistence. It does not mention side effects of promotion or whether runs overwrite previous results, but it is substantially transparent.

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 front-loaded with the main purpose and uses a tight bulleted structure. Every sentence adds distinct technical value, covering methodology, baselines, metrics, and persistence without filler.

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 thoroughly covers the validation methodology and evaluation metrics, but there is no output schema, no return format, no parameter guidance, and no usage context versus siblings. For a 4-parameter tool with no annotations and no output schema, this leaves notable gaps despite strong methodological detail.

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 0%, so the description needed to explain model, period, symbol, and n_folds, but it does not map any parameter to behavior. Parameter names and defaults are somewhat self-explanatory, which prevents a 1, but the description adds no explicit parameter meaning.

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 a specific operation: rolling-origin out-of-sample validation of time-series models, with concrete details such as multiple rolling origins, no look-ahead, next-bar prediction, and baseline comparisons. It does not explicitly differentiate itself from sibling tools like backtest or predict_*, so it stops short of 5.

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 guidance on when to use this tool instead of alternatives such as backtest, predict_chronos, or get_model_performance. The purpose is implied but no usage context, exclusions, or decision rules are provided.

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