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

Beyond the annotations, the description discloses meaningful scope limits: the verdict applies only to the series as submitted and the service never inspected data source, costs, survivorship, or lookahead. That tells the agent the result is a purely mathematical check. The readOnlyHint=false/openWorldHint=true annotations are neither repeated nor contradicted; the description adds real context rather than echoing them.

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 reasonably short (four sentences) and front-loads the outputs, but the first sentence is dense and awkwardly constructed, forcing re-reading to parse the three-output mapping. The disclaimer sentences earn their place, but the core sentence could be clearer.

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?

For a non-trivial portfolio-math tool, the description covers purpose, applicability, the three outputs' drivers, and the critical caveat about what data was never inspected. An output schema exists, so return-value explanation is not required. The only gap is the absence of explicit guidance on when to prefer this over a sibling validator.

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 coverage is 100%, so the baseline is 3, but the description adds the parameter-to-output relationship: sleeve_sharpe plus correlation determine the ceiling, sleeve count determines that book's Sharpe, and target determines the sleeve count needed. That pairing is not obvious from the schema alone, so it earns above baseline.

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 the three concrete outputs (book Sharpe ceiling, Sharpe at a sleeve count, sleeves required for a target), which is a specific computational purpose rather than a restatement of the name. It also differentiates itself from the sibling single-strategy validators with 'For portfolio construction; it validates no single strategy.' The opening sentence is grammatically elliptical ('Book Sharpe ceiling from adding sleeves...') but the intent is recoverable.

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?

It gives one usage signal — 'For portfolio construction; it validates no single strategy' — which implicitly routes the agent away from the sleeve-less validate_* siblings (validate_deflated_sharpe, validate_haircut_sharpe, etc.). However, it never names an alternative or states a when-not condition, so the selection guidance is implied rather than explicit.

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