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

Annotations already declare a safe read-only, closed-world, non-destructive operation, so the bar is lower. The description adds real behavioral value beyond that: the interpretation bands (H>0.5, H<0.5, H~0.5) and the critical input constraint that it takes a return series, not raw price levels, which directly affects correct invocation.

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 core computation, then usage triggers, then the input caveat and return shape. Dense but almost every clause earns its place; the interpretation parentheticals are slightly verbose but useful for interpreting output since no output schema exists.

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?

With no output schema, the description compensates by enumerating the return fields (hurst, interpretation, window_sizes, rs_values) and the meaning of the interpretation values. Combined with the input constraint and usage triggers, an agent has everything needed to call and interpret this tool.

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 100% and the input constraint 'Return series (not raw price levels)' is already present in the schema for values. The description restates this same constraint rather than adding syntax or guidance on min_window beyond what the schema default already provides, so baseline 3 applies.

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 verb and resource – computing the Hurst exponent via rescaled-range (R/S) analysis – and immediately distinguishes itself from siblings like run_garch and run_cointegration by naming the exact diagnostic it performs. The parenthetical interpretation thresholds give the agent a concrete sense of the output's meaning.

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?

Provides explicit trigger phrasing ('is this asset trending or mean-reverting?', 'does this series show long-range dependence?') that maps a user question directly onto the tool. It doesn't name alternative tools or when NOT to use it, but the triggering questions are specific enough to route correctly among the 60+ workflow siblings.

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