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

Annotations cover only the safety profile (readOnly, non-destructive, closed world), and the description adds rich behavioral detail: glm_converged=false means the fit may be unreliable and should be treated with caution, and bw_smoothing_detected=false pins the multiplier to 1 rather than emitting a misleading below-1 value. This edge-case disclosure is exactly the context annotations cannot provide.

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?

It is a long description, but with no output schema present, the enumerated return-field list is load-bearing rather than padding. The purpose statement is front-loaded before the field glossary, and each sentence carries meaning, though the single dense paragraph could be more scannable.

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 must explain return values, and it does so comprehensively, including convergence flags and the no-smoothing degenerate case. Nothing an agent needs in order to call this correctly or interpret its results appears to be 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?

Schema description coverage is 100% (the 'returns' array documents at-least-20-values), so the schema already carries parameter meaning. The description only echoes this with 'observed (possibly smoothed) return series' and adds no format or preprocessing detail beyond the schema. Baseline 3 applies when the schema does the heavy lifting.

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 ('Return unsmoothing') and immediately names the two models involved (Getmansky-Lo-Makarov 2004 MA(2) and Blundell-Ward 1987 AR(1)), plus the exact question it answers. An agent can distinguish it from siblings like run_garch or run_hurst_exponent without opening a schema.

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?

Gives explicit user-intent triggers ('how much is appraisal smoothing understating my real volatility?', 'what's my de-smoothed Sharpe ratio?') and scopes it to infrequently-marked/illiquid or appraisal-based series. It does not name a sibling alternative or a when-not-to-use condition, so it falls short of a 5.

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