Misata Studio: verified synthetic data

Export a dataset

export_dataset

Idempotent

Export a generated dataset as a file. Returns a `download_url` the person can open (or you can
fetch, e.g. with curl) for as long as the dataset is held, about 2 hours. Give the person the link
rather than pasting file contents into the chat.

Args:
    dataset_id: from a prior generate_dataset call.
    format:     data: csv, parquet, jsonl, json, avro, xlsx, feather, orc, sqlite, duckdb, sql.
                code and docs: dbt, notebook, dictionary, dbml, mermaid, prisma, sqlalchemy,
                typescript, jsonschema, expectations, django, openapi, mockapi, demo.
                `sql` is schema.sql (DDL with keys) + data.sql (COPY/INSERT) — the way to seed a
                real database: run the returned SQL through your own database connection, since
                this server never holds a database credential itself.
    dialect:    for `sql` only: postgres, mysql, sqlite, mssql, oracle, bigquery, snowflake.
    inline:     also return the file itself as `base64` (only for files under a few MB). Use it
                when you must write the file yourself and cannot fetch a URL.

Returns:
    filename, content_type, bytes, download_url, expires_at (unix seconds), and `base64` when
    `inline` and small enough.

Input Schema

TableJSON Schema

Name	Required	Default
`format`	No	csv
`inline`	No
`dialect`	No	postgres
`dataset_id`	Yes

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?

Goes well beyond the annotations (which only cover safety/idempotency) by disclosing the ~2 hour link lifetime, the `expires_at` field, the size ceiling on `inline`/base64, and the fact that the server never holds a database credential. These are exactly the operational traits an agent needs and that structured fields do not carry.

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 purpose, then Args, then Returns, and every sentence is load-bearing. The exhaustive format enumeration makes the block long, but since the schema supplies no enums that length is largely justified rather than padding.

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 documents the return payload (filename, content_type, bytes, download_url, expires_at, conditional base64), and it covers the auth/credential boundary and lifetime. Nothing needed to invoke it correctly is missing.

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 description coverage is 0%, so the description carries the full burden and does so: it enumerates every `format` value grouped by data vs code/docs, explains what `sql` produces and how to consume it, restricts `dialect` to `sql` with its valid values, and scopes `inline` to small files. This fully compensates for the undocumented schema.

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 specific verb and resource ('Export a generated dataset as a file') and ties the artifact back to a named sibling ('from a prior generate_dataset call'), so an agent can place it in the workflow without opening the schema. It also distinguishes the deliverable (a link) from what the agent should do with it.

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?

Gives explicit when-to-use direction: hand the person the link rather than pasting contents, use `inline` only when you must write the file yourself and cannot fetch a URL, and for `sql` run the returned DDL/DML through your own connection. The `sql` note effectively rules out an alternative (expecting the server to hold credentials).

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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