Preview how a story is interpreted

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds context: it clarifies no rows are generated, that rows affects preview only, and lists returned fields (domain, confidence, near-misses, locale, scale, tables). 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 well-structured: a one-sentence purpose, a list of returned items, a usage note, and an Args section. It is concise with no redundant text and front-loads the core purpose.

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 simplicity (2 params, output schema exists), the description covers purpose, usage, parameters, and return values. It provides sufficient context for an agent to understand when and how to invoke it, without needing to rely on the output schema.

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 0%, so the description must clarify parameters. It provides Args: 'story: Plain-English description of the dataset' and 'rows: Default row count for the primary table (affects preview only).' This fully compensates for missing schema descriptions.

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 clearly states the tool's purpose: 'Inspect what Misata would generate from a story — without generating any rows.' This uses a specific verb (inspect) and resource (story interpretation) and differentiates from sibling generation tools like generate_dataset and seed_database.

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?

Explicitly says when to use: 'Use this to confirm interpretation before committing to a (potentially large) generation.' It implies the alternative is to generate, but does not name the exact sibling tools. Still, the guidance is clear and actionable.

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