Misata

You declare the outcome. Misata generates the data that provably matches it.

Realistic, relational rows that hit exact revenue curves, fraud rates, referential integrity, and statistical structure. From a sentence, YAML, or your database. No real data, no ML model.

Prefer no code? Try Misata Studio, the no-code synthetic data generator: design a schema on a canvas or describe your dataset in plain English, then generate it in your browser. Same engine, same integrity proof.

Most synthetic-data tools learn from a real dataset and imitate it. Misata works the other way: you declare the outcome you want: "monthly revenue rises from $50k to $200k," "fraud is 3% in Q1 rising to 8% by Q4," "every customer's total_spent equals the sum of their orders", and Misata generates individual rows whose aggregates hit those targets exactly, with full referential integrity, from no source data at all.

This is outcome-conformant generation. The mechanism is formalised in an arXiv preprint (2606.08736): a closed-form method that satisfies declared aggregates to $0.00 error, where off-the-shelf imitation synthesisers trained on the same data miss by 74–86%. Every run can also emit an Oracle report, a proof bundle covering referential integrity, constraints, temporal consistency, and reproducibility.

It generates from a plain-English description, a YAML schema, or an existing database schema. No machine-learning model is required. No real data is needed.

Built for:

• Known-answer testing: declare the KPI, generate the data, then assert your dbt, Spark, or SQL transform returns exactly that number. A pipeline test with a ground truth, before any real data exists
• Database seeding: fill dev and staging environments with production-like data
• Integration tests: relational fixtures with FK integrity across every table
• Demos and prototypes: realistic numbers, names, and distributions, no PII
• BI and dashboard development: data shaped like your real domain before launch
• Statistical method validation: longitudinal, grouped, and multi-site datasets that pass mixed-effects models, ICC tests, and autocorrelation checks

Declare or mimic: two ways in

Misata works in two modes, and the difference is the whole point:

• Declare (the default, no data required). You state the schema and the outcomes you want, exact revenue curves, fraud rates, rollups, constraints, and Misata generates rows from scratch that conform to them. Use this when you do not have real data, or when you need a known answer to test a pipeline, dashboard, or demo against.
• Mimic (when you already have data). Point misata.mimic() at a real CSV and get a synthetic twin that matches its distributions and correlations but contains none of the original rows, with fidelity_report and privacy_report to measure the result. Use this for privacy-safe copies of data you already hold.

Most synthetic-data tools only do the second, learning from a real dataset and imitating it. Misata leads with the first: you declare the answer, then generate the data around it.

Research

Misata's exact-aggregate engine is backed by an arXiv preprint:

Declarative Outcome-Conformant Synthesis: Exact, Closed-Form Specification Satisfaction and a Conformance Benchmark
Muhammed Rasin, arXiv:2606.08736 (2026)
https://arxiv.org/abs/2606.08736v1

The paper formalises the core claim: when you declare "SaaS MRR from $50k in January to $200k in December", Misata generates individual transactions whose monthly totals match the declared curve to exactly $0.00 error, not approximately, but provably, via a closed-form Gamma conditional-sum mechanism (Lukacs' characterisation). Off-the-shelf imitation synthesisers trained on the very same data miss the declared monthly aggregate by 74–86%; Misata reaches exactly 0.

The paper also introduces SpecBench: the first benchmark measuring conformance to analytical outcomes for cold-start relational synthesis. Misata is the reference implementation.

@article{rasin2026declarative,
  title   = {Declarative Outcome-Conformant Synthesis: Exact, Closed-Form
             Specification Satisfaction and a Conformance Benchmark},
  author  = {Rasin, Muhammed},
  year    = {2026},
  url     = {https://arxiv.org/abs/2606.08736v1}
}

Install

pip install misata

Optional extras:

pip install "misata[llm]"        # multi-provider LLM schema generation
pip install "misata[documents]"  # PDF output via weasyprint
pip install "misata[advanced]"   # SDV/CTGAN statistical synthesis
pip install "misata[mcp]"        # MCP server, expose Misata to Claude, Cursor, and other AI agents
pip install "misata[evalpack]"   # evalpacks: verified eval databases for data agents (DuckDB)

Use it from a coding agent

Misata ships an Agent Skill, so Claude Code and anything else that reads SKILL.md knows which entry point fits which request and what is worth declaring:

/plugin marketplace add rasinmuhammed/misata
/plugin install misata@misata

The skill drives the CLI, so pip install misata is still required. There is also an MCP server (pip install "misata[mcp]") and a Claude Desktop extension in mcpb/.

Use Misata from Claude / Cursor / Windsurf (MCP)

Misata ships a built-in Model Context Protocol server with a clear division of labour: the AI agent designs the schema, Misata guarantees the math. Agents are good at knowing that a veterinary clinic needs a species column; Misata is good at making 50 000 rows where every foreign key resolves, every roll-up reconciles to the cent, and the same seed reproduces byte-identical output. The primary tool, generate_from_schema, accepts the agent's schema dict and returns the data plus an integrity proof: per-relationship orphan counts the agent can show you.

1. Install:

pip install "misata[mcp]"

2. Add to Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "misata": {
      "command": "misata-mcp"
    }
  }
}

Restart Claude Desktop. Then just ask:

"Generate a fintech dataset with 1 000 customers, payments, and a 2% fraud rate."

"Design a clinical-trials database (sites, patients, visits, adverse events) and generate 100k rows."

"I need SaaS data: MRR from $50k in January, doubled by December, with a Q3 slump."

The agent designs whatever tables the request needs (any domain; it isn't limited to Misata's built-ins), calls Misata, writes CSVs to disk, and reports back with previews and the verified integrity summary. See the MCP guide for Cursor/Windsurf/Zed setup and all six available tools.

mcp-name: io.github.rasinmuhammed/misata

Quick start

misata generate \
  --story "Brazilian fintech with R$ payments, CPF verification, and 3% fraud" \
  --rows 1000 \
  --output-dir ./demo_data

# Writes CSVs plus:
# ./demo_data/oracle_report.json

import misata

# One sentence → multi-table DataFrame dict
tables = misata.generate("A SaaS company with 5k users, monthly subscriptions, and 20% churn")

print(tables["users"].head())
print(tables["subscriptions"].head())

# Or from the CLI
misata generate --story "A SaaS company with 5k users and 20% churn" --rows 5000

Misata Oracle

The Oracle report is Misata's proof layer. It separates hard guarantees from advisory realism checks so generated data can be trusted in CI, demos, notebooks, and research comparisons.

Guaranteed checks:

• referential integrity across configured relationships
• requested row-count fulfillment
• schema validation and configured constraints
• deterministic reproducibility when a seed is set

Advisory checks:

• quality score and plausibility warnings
• privacy heuristics
• schema-vs-output fidelity score
• locale/domain fit for countries, cities, phone prefixes, and national IDs
• data-card metadata

import misata

schema = misata.parse("Brazilian fintech with CPF verification", rows=1000)
tables = misata.generate_from_schema(schema)
oracle = misata.build_oracle_report(tables, schema, seed=schema.seed)

print(oracle["passed"])
print(oracle["advisory"]["locale_domain_fit"]["locale"])

Mimic mode: clone any CSV in one call