cross_validate

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

Annotations declare readOnlyHint=true and openWorldHint=false. The description adds the key behavioral trait: it runs multiple independent engines and reports an agreement verdict, plus the rule for trusting a number. It does not contradict annotations. It does not detail the exact response structure beyond the enum, but the output schema (present) covers that. Overall it provides meaningful behavioral context beyond the 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 a compact three sentences with zero waste. The purpose is front-loaded, the usage guidance follows immediately, and the data_path requirement is stated last. Every sentence earns its place.

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?

Given the tool's complexity (19 parameters, 6 estimand types) and the existence of an output schema, the description covers the essential context: what it does, when to use it, and the key requirement (data_path). It does not explain the meaning of each estimand or the exact output fields, but the schema and output schema handle that. It is complete enough for an agent to decide and call it correctly.

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 79%, so the schema already documents most parameters. The description only adds a note about data_path being needed, which is redundant with the required list (though data_path is not required, only estimand is required). It does not add meaningful semantic detail beyond the schema for the parameters. Baseline 3 is appropriate.

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 verb ('cross-validate'), a specific resource ('ONE estimand across INDEPENDENT engines'), and enumerates the engines (StatsPAI, pyfixest, linearmodels, DoubleML, R's fixest, Stata). It also names the output categories (AGREE / PARTIAL / DISAGREE / INSUFFICIENT), making the tool's function unambiguous and distinct from the many siblings that focus on single estimators or diagnostics.

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 gives explicit when-to-use guidance: 'Use this to honour the cross-package reproducibility rule: trust a number only when >=2 independent implementations reproduce it.' It also notes the need for data_path. It does not explicitly name alternatives for when not to use it, but the context is clear enough to route an agent correctly.

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