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Inspect the schema behind a story

inspect_schema
Read-only

Return the complete table schema with column types and relationships for a dataset described in plain English, including distribution parameters for synthetic data creation.

Instructions

Return the full schema (tables, columns, relationships) for a story without generating data.

Heavier than preview_story — includes every column with its type and distribution params. Use when the user wants to see the structure they'll get, or to author a misata.yaml file from a natural-language seed.

Args: story: Plain-English description of the dataset. rows: Default row count for the primary table.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsNo
storyYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already mark this as read-only and non-destructive; the description builds on this by noting it does not generate data and by explaining the type of output (every column with type and distribution params). It stops short of detailing performance implications beyond 'heavier' or clarifying idempotent behavior, but adds meaningful context beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is tightly written, starting with a clear one-sentence purpose, followed by a comparative detail, use cases, and a compact Args list. Every sentence earns its place with no redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only inspection tool with two parameters and a documented output schema, the description covers the what, when, and parameters, and even notes the lack of data generation. It could go further by describing error scenarios or output format specifics, but these are already captured by the output schema and annotations, making it adequately complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description must compensate, and it does via an Args section that explains `story` as 'Plain-English description of the dataset' and `rows` as 'Default row count for the primary table.' This adds clear meaning beyond the bare types, though it could elaborate on how changing `rows` affects its behavior. The high coverage shift justifies a score above baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Return the full schema (tables, columns, relationships) for a story without generating data,' using a specific verb and resource scope. It goes beyond a generic statement by explicitly contrasting with `preview_story` ('Heavier than') and providing concrete use cases, making its purpose unmistakable and distinctive.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly notes this tool is 'Heavier than `preview_story`' and provides two direct use cases: showing the structure the user will get and authoring a `misata.yaml` file from a seed. This gives an agent clear when-to-use guidance and signals an alternative, even if it doesn't mention explicit exclusions.

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

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