Misata: synthetic data library MCP server

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.

<!-- mcp-name: io.github.rasinmuhammed/misata -->

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 totalspent 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:

Shortened. The full README is on GitHub.