Validate a dataset against domain rules

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds significant behavioral context beyond that: the exact output structure (passed, errors, warnings), the meaning of 'passed', and the built-in domain ranges. It also clarifies that it does not check internal consistency, preventing misinterpretation. No contradictions 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 well-structured: a one-sentence summary, a clarifying contrast, domain-specific ranges, usage guidance, and a clean Args/Returns section. It is front-loaded with the core purpose and every sentence serves a distinct function. No fluff or repetition.

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?

Despite having an output schema, the description still details the return format and the logic for 'passed'. It covers parameter semantics, domain values, file structure, and usage timing. For a tool with only two parameters and a well-defined output schema, this is complete and leaves no ambiguity for the agent.

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 0%, so the description carries the full burden. It explains dataset_dir as 'Directory containing one CSV per table' and domain as one of four enumerated values. This fully compensates for the schema's lack of descriptions, giving the agent everything needed to populate both parameters correctly.

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 opens with a precise verb and resource: 'Flag values that are physiologically or financially impossible for a domain.' It immediately contrasts with the sibling audit_dataset, distinguishing its purpose clearly. The specific built-in ranges reinforce the scope, making the tool's function unmistakable.

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 explicitly states when to use: 'after generating a dataset in a regulated or measurement-heavy domain' and when the user asks about plausibility. It also names the alternative audit_dataset and differentiates by checking 'internal consistency' vs 'outside world' values. This gives the agent clear routing criteria.

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