Misata Studio: verified synthetic data

Plan a dataset

plan_dataset

Read-onlyIdempotent

See the tables, sizes and relationships the engine would build, before any rows exist. Free (no
rows are made), so it is worth calling before generate_dataset on anything non-trivial: review
what it understood and assumed, then adjust your request or schema before spending a real call.

Args:
    request: Plain-English description (needs an LLM key, unless `schema`/`ddl` is also given —
             then it is still used to ground realism, e.g. locale and what columns mean).
    schema:  A Misata schema dict (see the server instructions for the format). No key needed for
             structure.
    ddl:     CREATE TABLE statements. No key needed for structure.

Returns:
    route:         "chat" (not a dataset request — see `reply`), "design" (the model designed the
                   tables) or "pack" (matched a built-in shape).
    tables:        name, estimated rows, columns, foreign keys for each table the engine would build.
    understanding: what the engine read the request as (business, archetype, assumptions).
    requirements:  every specific thing the request asked for, so you can see what was understood.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`ddl`	No
`schema`	No
`request`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 1, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Annotations already cover safety (readOnly, idempotent, non-destructive), yet the description still adds real behavioral context: the call is free and creates no rows, and an LLM key is required for `request` unless `schema`/`ddl` are supplied. The three possible `route` outcomes further disclose how the call actually resolves.

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

Conciseness4/5

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

Front-loaded with the purpose and cost argument in the first sentence, then structured Args/Returns sections. Slightly verbose in the Args block, but every line conveys non-obvious information.

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

Completeness5/5

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

No output schema exists, so the Returns block is genuinely necessary and enumerates route, tables, understanding, and requirements. Combined with annotations and param docs, an agent has everything needed to call and interpret this tool correctly.

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 carries the burden and does so well: it explains each of the three params (plain-English request needing a key, Misata schema dict, CREATE TABLE ddl) and their key requirements. Minor deduction because the schema dict format is deferred to 'server instructions' rather than summarized.

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?

States a concrete outcome — 'See the tables, sizes and relationships the engine would build, before any rows exist' — which is a dry-run preview distinct from every sibling. An agent can instantly distinguish it from generate_dataset without opening the schema.

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?

Explicitly says to call it 'before generate_dataset on anything non-trivial' and names the alternative it precedes. It also tells the agent the follow-up action: review assumptions, then adjust the request or schema.

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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