Misata Studio: verified synthetic data

Validate a blueprint

validate_blueprint

Read-onlyIdempotent

Check a blueprint before generating it: every error said as what to change, the design traps it
falls into (a column that will round away, a copy of a value not made yet, a window that counts
nothing), its size against your row cap, and a small preview run (a few thousand rows) with the
verifier's findings and sample rows, so you can see the data behave before the real call. Free.

Returns:
    valid, errors (fix these), warnings (read these), planned (sizes the stated numbers set; "keep_size"
    on a table keeps its row_count), estimated_rows, row_cap,
    preview: {passed, findings, tables: {name: first rows}} when `preview`.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`preview`	No
`blueprint`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 1, 2026

TDQS

A3.9/5.0

Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive, closed-world behavior, so the bar is lower. The description adds real context beyond them: the preview is only a few thousand rows, the call is free, errors are framed as actionable ('said as what to change'), and warnings are separately characterized as 'read these'.

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-loads the one-line purpose, then the checks, then a Returns block. The opening sentence is long and comma-dense, but every clause carries distinct information and nothing is padding.

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?

No output schema exists, and the description compensates with an explicit Returns list (valid, errors, warnings, planned, estimated_rows, row_cap, preview findings/tables), which is genuinely helpful. Annotations cover the safety profile. The only real gap is the undefined blueprint object structure, which sibling blueprint_guide likely addresses.

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

Parameters3/5

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

Schema description coverage is 0% for two parameters, so the description carries the burden. It partially compensates — it references the `preview` parameter ('when `preview`') and exposes blueprint field semantics ('keep_size' on a table keeps its row_count) plus the shape of traps (columns, window functions) — but it never defines what a blueprint object is or its structure, leaving the main required parameter under-specified.

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

Purpose4/5

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

States a specific verb and resource ('Check a blueprint before generating it') and enumerates exactly what it checks — errors, design traps, size against row cap, preview run. Its placement relative to generation ('before the real call') implicitly distinguishes it from generate_dataset, though it never names that sibling outright.

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

Usage Guidelines4/5

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

Gives a clear condition for use: run it before generating, to see the data behave before the real call. It also notes the operation is 'Free', which is a meaningful cost signal for deciding whether to run it. It stops short of naming alternatives or explicit when-not-to-use cases.

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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