Misata Studio: verified synthetic data

Find a ready-made dataset

find_ready_dataset

Read-onlyIdempotent

The ready-made datasets Misata publishes: free sample databases (direct download, public domain)
and premium datasets with a full answer key (a free preview, then a one-off price). Check this
first when someone wants sample, demo, practice or teaching data for a common scenario (retail,
e-commerce, SaaS, manufacturing SPC, predictive maintenance, insurance claims, fraud/AML,
clinical, network security): handing over a dataset that already exists is instant. If none fits
their tables, generate one instead.

Args:
    query: what the person needs, in their words. Only orders the list (closest first); every
           dataset is still returned, so judge the fit yourself from the tables and summary.

Returns:
    datasets: [{slug, kind (free|premium), title, summary, rows, tables, page_url, and
    download_url (free) or free_preview_url + buy_url + price_usd (premium)}].

Input Schema

TableJSON Schema

Name	Required	Description	Default
`query`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 1, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive, and the description adds real behavioral context beyond them: the query does not filter (every dataset is always returned, only ordered), free datasets are public-domain direct downloads, premium ones are preview-then-one-off-price, plus the exact returned fields including download/preview/buy URLs and price. No contradiction with readOnlyHint.

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?

Well front-loaded (purpose, then when-to-use, then Args/Returns) and uses clear sections. Slightly padded by the long parenthetical scenario list and the 'instant' aside, but nothing is misleading or wasted enough to harm selection.

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?

With no output schema, the description supplies the return shape (datasets array with slug, kind, title, summary, rows, tables, page_url, and the free/premium download fields), plus the non-filtering semantics. An agent has everything needed to call it and interpret results.

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

Parameters5/5

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

Schema coverage is 0% (no description on `query`), so the description carries the full burden and does it well: it is free-text in the user's words, it only orders results closest-first, and it does not filter — telling the agent to judge fit itself. That is meaning no schema field conveys.

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?

Specific verb+resource: publishes/finds ready-made datasets, split into free sample DBs and premium datasets with answer keys. It explicitly distinguishes itself from the generate path ('If none fits their tables, generate one instead'), so an agent can separate it from generate_dataset without opening either 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?

'Check this first when someone wants sample, demo, practice or teaching data for a common scenario' gives an explicit trigger, and the fallback ('generate one instead') names the alternative action with its condition. The named scenario list further pins down when this applies.

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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