Validate breadth ceiling

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

Annotations are present (readOnlyHint=false, openWorldHint=true, destructiveHint=false). The description adds transparency by stating the service never saw the data source, costs, survivorship, or lookahead, which clarifies the scope and limitations of the verdict. This goes beyond the annotations and provides valuable behavioral context. No contradiction with annotations.

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?

The description is two sentences, but the first sentence is a single complex clause listing three computed outputs. It is efficient without wasted words, though the list could be more clearly structured. The scope clarification in the second sentence is valuable. It is appropriately sized for a tool with 4 parameters.

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?

The description explains what the tool computes (highest Sharpe, Sharpe at a count, sleeve count for target) but does not explicitly describe the output format or structure. Since there is no output schema, the description should clarify what the verdict looks like (e.g., a numeric result, a report, a pass/fail). The mention of 'verdict' hints at a result, but lacks detail. For a validation tool with no output schema, this is a notable gap.

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 all parameters have descriptions. The description enhances this by linking 'quality' to sleeve_sharpe and 'correlation' to average_pairwise_correlation, and by clarifying the roles of 'target' and 'sleeves' as inputs for specific outputs. This adds semantic meaning beyond the schema definitions.

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 a specific purpose: validating a breadth ceiling by computing the highest attainable book Sharpe given sleeve quality and correlation, plus Sharpe at a given sleeve count and the sleeve count needed for a target. The verb 'validate' and resource 'breadth ceiling' are clear, though the multi-output nature is somewhat dense. It distinguishes itself from sibling validation tools by focusing on breadth, but does not explicitly name alternatives.

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?

The description implies this tool is for validating a series exactly as submitted, clarifying that it does not account for external factors. However, it gives no explicit guidance on when to use this tool versus the sibling validation tools (e.g., validate_deflated_sharpe, validate_overfitting). The usage context is implied but not directly articulated with alternatives or exclusions.

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