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.
                `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.
                `demo` is the handover for a demo or an eval: the database with a one-step loader,
                the ER diagram, answer_key.json (every number by month), questions.jsonl and
                the proof of each request.
    dialect:    for `sql`: postgres, mysql, sqlite, mssql, oracle, bigquery, snowflake, clickhouse
                (MergeTree tables; above 500k rows, typed Parquet with load.sql). For `demo`:
                postgres (psql) or clickhouse (Parquet loaded with clickhouse-client).
    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

A4.6/5.0

Behavior4/5

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

Annotations already declare idempotent=true and destructive=false, but the description adds substantive behavior beyond them: the download_url lives only ~2 hours, `inline` works only for files under a few MB, and the server never holds a database credential so SQL must be run through the caller's own connection. Minor gaps remain (what happens on expiry, retry behavior).

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?

Front-loaded with purpose and the critical return value, then organized into clean Args and Returns sections. Despite its length, every line resolves a real ambiguity (format semantics, dialect scope, inline size limit) 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?

There is no output schema, so the description appropriately enumerates the return fields (filename, content_type, bytes, download_url, expires_at, base64). Combined with parameter semantics and expiry/credential caveats, an agent has everything needed to call and use this correctly.

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?

With 0% schema description coverage the description carries the full load and does so thoroughly: it explains dataset_id's provenance, enumerates valid `format` values grouped by data vs code/docs and explains special cases (`sql`, `demo`), and restricts `dialect` to the relevant formats with concrete options. A bare schema with only defaults would leave an agent guessing.

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 first sentence names a specific verb and resource ('Export a generated dataset as a file') and immediately clarifies the output (a download_url). It is trivially distinguishable from siblings like generate_dataset or query_dataset, which produce/query rather than export.

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 explicit usage guidance at the point of decision: hand the person the link rather than pasting contents, and use `inline` only 'when you must write the file yourself and cannot fetch a URL.' It also anchors `dataset_id` to a prior generate_dataset call. It does not explicitly contrast with sibling tools or state when not to export.

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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