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

Goes well past the readOnly/idempotent annotations: names the statistic (Spearman rank correlation), the target (forward PnL over account value), the significance bar (p<0.05), the full verdict taxonomy including inverted and insufficient_data, and states that cohorts are server-selected so callers cannot steer results. That last point is a real anti-gaming disclosure an agent could not infer elsewhere.

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?

Front-loaded with the purpose in the opening clause, then enumerates the method, verdicts, and sample-integrity caveat. Dense and slightly run-on, but every clause carries information an agent needs; nothing is 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?

An output schema exists so return structure needn't be described, yet the description still explains what the verdict labels mean and what would make the result untrustworthy (too few resolved observations). Nothing needed to interpret or trust a call is missing.

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?

Zero parameters, so the baseline is 4; the description still usefully explains that sample composition (which cohorts) is server-controlled rather than caller-supplied, closing the question of why there is no input schema.

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?

States a specific resource (the hl_smart_money_score) and a specific operation (out-of-sample calibration: Spearman correlation of score vs. realized forward return, plus per-quartile means). It is clearly distinct from the sibling hl_smart_money_score, which produces the score itself, and from hl_signal_track_record/hl_trader_report.

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?

"Read this before treating the score as a forecast" gives an explicit trigger condition, and the framing of the tool as validation vs. production of the score implies the alternative. No explicit when-not or prerequisite/ordering statement beyond that.

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