run_feature_outcome_power_audit

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

With no annotations, the description carries the behavioral disclosure burden. It transparently states that the tool injects a synthetic effect, measures detection rates, and that output is sensitivity evidence rather than alpha. It does not mention side effects or persistency, but the core synthetic experimental behavior is clearly conveyed.

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 compact at two sentences and front-loads the core measurement action, with the important 'not market alpha' caveat placed second. However, the dense jargon around 'frozen feature-outcome candidate gate' and 'injected after one body-direction bucket' harms readability, though every phrase contributes meaning.

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?

Given 26 parameters, no output schema, no annotations, and zero schema description coverage, the description is far from complete. It establishes purpose but omits required parameter guidance, output expectations, operational constraints, and any invocation prerequisites, making it insufficient for correct use.

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%, and the description provides no parameter-level guidance. Terms like 'predeclared signed next-bar effect' and 'body-direction bucket' only weakly map to effect_bps, horizons, or target_bucket, leaving the agent unable to construct a valid call for a 26-parameter tool with 8 required parameters.

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 verb and target: 'Measure how often the frozen feature-outcome candidate gate detects a predeclared signed next-bar effect injected after one body-direction bucket.' It also clarifies this is synthetic sensitivity evidence, not market alpha, which distinguishes it from sibling falsification audits and outcome studies.

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 implies use for synthetic sensitivity testing and warns against treating results as market alpha, but it never names sibling alternatives or gives explicit criteria for when to choose this audit over run_feature_outcome_falsification_audit or compute_feature_outcome_relationships. The context is clear but exclusions are absent.

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