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

Annotations (readOnlyHint=false, destructiveHint=false, openWorldHint=true) are somewhat at odds with an obvious pure-calculation tool, and the description neither confirms nor contradicts them. It does add interpretive context ('not admission to anything and is not a forecast') and discloses that output includes observations/years, but says nothing about precision, numerical caveats, or failure modes.

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?

Three sentences, front-loaded with the core computation and then the sibling routing. The final disclaimer sentence is useful but slightly tangential and the first sentence is dense with nested clauses, costing a little clarity.

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 an output schema present and full schema coverage, the description does not need to explain return values, and it covers purpose, scope, and routing. It is complete enough to invoke correctly, though it never explains what the periodic/year conversion or confidence inputs imply about results.

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%, so the schema already defines every parameter, including defaults (benchmark 0, confidence 0.95). The description names 'observations', 'confidence level', and benchmark only in passing and adds no format or edge-case semantics beyond the schema, 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 computation (minimum track record length, in observations and years) on a named resource (an observed Sharpe vs a benchmark) and explicitly names the sibling it is not: validate_backtest_length. An agent can distinguish it from the several validate_* siblings without opening the 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 an explicit when ('For live or paper records') and an explicit alternative with its selecting condition ('to size a backtest for its trials, use validate_backtest_length'). It also warns what the tool is not for, which is exactly the routing guidance an agent needs among near-identical validate_* siblings.

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