backtest

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

With no annotations, the description carries the full burden of behavioral disclosure. It discloses the computation style (walk-forward), data source, and the exact output metrics/baselines, but it does not mention side effects, network/live-data dependency, execution time, or failure behavior.

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?

Two dense, front-loaded sentences cover the core behavior and output without redundancy. Every phrase earns its place.

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 4-parameter tool with no output schema and no annotations, the description is incomplete: it omits parameter semantics, usage boundaries, and operational realities. An agent could call it with defaults, but customizing the invocation requires guesses.

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_splits. It does not define any of them; the defaults are visible but their meaning must be inferred from the parameter names and the phrase 'walk-forward backtest.'

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 states a specific action ('walk-forward backtest'), a data source (yfinance), and the classifier-based methodology. It also enumerates the output metrics and baseline values, making it easily distinguishable from the sibling prediction and analysis tools.

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 intended use is implied by the name and purpose: run a walk-forward backtest on yfinance data. However, it does not explicitly state when to prefer this tool over siblings like run_ts_validation or predict_ensemble, nor does it mention when not to use it.

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