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Misata: synthetic data library

by rasinmuhammedversion 0.9.8i★ –iRuns on your computer
Registry nameio.github.rasinmuhammed/misata

Install

Overview

Description from the maker (original English)

Declarative synthetic data: multi-table data that holds the facts you declare, checked by DuckDB.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Databases & datasets

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "misata": {
      "command": "uvx",
      "args": [
        "misata"
      ]
    }
  }
}

Run this in your terminal

claude mcp add misata -- uvx misata

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "misata": {
      "command": "uvx",
      "args": [
        "misata"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "misata": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "misata"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

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Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account rasinmuhammed, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Databases & datasetsi

  • More stars than 0% of the other 1144 servers in this category
  • Published to the registry more recently than 75% of the other 1144 servers in this category
  • Basic checks equal to or better than 100% of the other 1144 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Misata: synthetic data library safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.9.8) reached the registry 12 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Supabase, MongoDB MCP Server, Elasticsearch MCP Server

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Misata

Declare what must be true. Get data that makes it true. Prove it.

Misata is a declarative synthetic data generator for Python. You write down the facts a dataset has to satisfy, from a monthly revenue curve to a balanced ledger to a foreign key that must never dangle, and the engine builds multi-table data that satisfies all of them at once. Then DuckDB checks the files on disk and writes a certificate anyone can re-run.

Docs · Use cases · The language · Limitations · Studio · Changelog


Sixty seconds

pip install "misata[duckdb]"
# misata.yaml

seed: 7 tables: customers: rows: 2000 columns: customer_id: {type: int, unique: true, min: 1, max: 2000} name: {type: text, text_type: name} signed_up: {type: date, start: "2024-01-01", end: "2024-12-31"} orders: rows: 40000 columns: order_id: {type: int, unique: true, min: 1, max: 1000000} customer_id: {type: foreign_key} channel: {type: categorical, choices: [web, app, store]} amount: {type: float, min: 5, max: 900} ordered_at: {type: date, start: "2025-01-01", end: "2025-12-31"} relationships:

  • customers.customer_id -> orders.customer_id

outcome_curves:

  • table: orders column: amount time_column: ordered_at start_date: "2025-01-01" curve_points:
  • {month: 1, target_value: 180000}
  • {month: 6, target_value: 240000}
  • {month: 12, target_value: 310000}

group_shares:

  • table: orders measure: amount group_column: channel shares: {web: 0.5, app: 0.3, store: 0.2}
misata generate -c misata.yaml -o data --attest

python data/verify.py

Attestation: data

result: PASSED files pinned: 2 invariants held: 69 of 69

ok 2 file(s) match their attested sha256 ok 69 of 69 invariant(s) hold

The 69 are derived from the YAML before a row exists: twelve monthly revenue totals (180,000.00 in January, 240,000.00 in June, 310,000.00 in December, straight lines between), the channel split inside every one of those months, the planned row count per month, zero orphaned customer_ids and unique keys. verify.py needs only the standard library and duckdb, and it shares no code with the generator.

The YAML above, generated and summed by month and channel. Each bar is a month of generated revenue split by channel; the cap is the total the YAML declares. Drawn by readme_charts.py from a fresh run of this README's own YAML.

Change one cent in one row and it says so:

FAIL  orders.csv: sha256 301165f9dc4e does not match the attested 670f89195826

FAIL orders.amount.month:1.outcome_curve_period: expected 180000.00, observed 180000.01 FAIL orders.amount.outcome_curve_total: expected 2945000.00, observed 2945000.01 FAIL orders.amount.web.2025-01.group_share_period: expected 90000.00, observed 90000.01 FAIL orders.amount.web.group_share_total: expected 1472500.00, observed 1472500.01 FAIL 65 of 69 invariant(s) hold


Why declare instead of generate

Every test fixture has an oracle problem. To check a dbt model, a Spark job, a dashboard tile or a data agent you need input data and an answer you know independently of the code under test. Copying production gives you data without an answer. Hand-written fixtures give you an answer that rots. Random generators give you data whose answer you have to compute with the code you are testing.

Misata turns it around. The specification is the ground truth and the rows are derived from it. "January revenue is 180,000" is true before a single row exists, so your pipeline either returns 180,000.00 or it has a bug.

flowchart LR

A["Declare curves, shares, ledgers, keys, lifecycles"] --> B{"Can they all hold?"} B -- "no" --> R["Refused, with the arithmetic and a fix"] B -- "yes" --> C["Build rows closed form where it can, by construction elsewhere"] C --> D["Write CSV, Parquet, JSONL, DuckDB, Postgres"] D --> E["Attest DuckDB re-runs every derived invariant"] E --> F["Certificate in-toto Statement plus verify.py"]

Three properties make this different from a generator with options:

  • A declaration is honoured or refused, never quietly replaced. Declarations that cannot all hold stop generation with an error that names each one, shows the arithmetic and suggests the change.
  • The certificate is checked by code that did not write the data. A bug in Misata's arithmetic shows up as a failed invariant, not a self-consistent wrong answer.
  • The same spec, seed and version give the same bytes on Linux, macOS and Windows, so a fixture can live in git with its fingerprint.

Where this is going

Misata's aim is that nobody writes a Python script to make a dataset, of any kind. You say what has to be true; the engine works out the rows, refuses what cannot hold, and proves what it built.

It is not there yet, and it is getting there brick by brick. Each release adds things you can declare instead of programming, and a brick is laid only when it passes three tests: it can be declared in a few lines of YAML, it is refused before generation when it contradicts another declaration, and it is re-checked on the rows afterwards by code that did not write them.

Every brick is something you declare instead of code. Drawn from misata/registry.py , the list a contract test holds the code to, so the wall cannot claim a brick the engine does not have.

One fact, two ways. What a script has to do to make a fact true, and what you write instead:

The factIn a generator scriptIn Misata
January revenue is 180,000.00draw amounts, sum them, rescale, round, chase the last cent{month: 1, target_value: 180000}
web is half of every month's revenuerescale inside each month and channel, then repair the monthly totals you just brokeshares: {web: 0.5, app: 0.3, store: 0.2}
every order has a real customersample ids from the parent table and keep both in stepcustomers.customer_id -> orders.customer_id
some churned customers were active firstbranch on status, write timestamps in order, null the rest, count by handtransitions: [[trialing, active], [active, churned]]
1% of transfers form laundering patterns, in a set mixbuild each ring and fan by hand, then prove the background has no cycle of its ownrate: 0.01 and shares: {cycle: 0.4, fan_in: 0.25, ...}
the trial balance is zeropost both legs of every entry and reconcile at the enda balanced_ledger constraint on the journal lines

What still takes code, and where each one stands:

Still scripted by handWhere it stands
The expected output of logic Misata cannot invert: window functions, custom SQL, business rulesmisata solve drives your real dbt, SQL or Python pipeline to a declared number within a stated tolerance; past that, you write the expected value
Correlations you can only describe by examplemisata.mimic fits them from real rows, and its output is not anonymous
Free text with a voice of its owntext is generated from grammars and agrees with its row; prose that reads as written is not declarable yet
Version histories (SCD2) and inventory trajectoriesgenerated to balance, not yet declared step by step
Documents, images and audionot started

LIMITATIONS.md keeps the full list. When a line leaves this table, it is because the brick that replaces it has passed all three tests.


The evidence

The Gauntlet: 13 tables and 136 SQL assertions run by DuckDB. The Faker script ties wherever a fact is about one column at a time and loses wherever columns, rows or tables have to agree. Source , rerun with python -m benchmarks.gauntlet .

<img src="https://raw.githubusercontent.com/rasinmuhammed/misata/main/docs/assets/readme/detection-light.svg" width="680" alt="Detection AUC, lower is better, 0.5 means a classifier cannot tell generated rows from real ones. Olist orders: Misata from a declared schema 0.63, Misata from a sentence 0.73, Faker script 0.78, SDV fitted to the real data 0.71, Misata mimic 0.54. NYC taxi trips: 0.

Only the beginning is shown; read the rest in the repo

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