Misata: synthetic data library

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Works with Claude Code, Codex, VS Code, Cursor, Windsurf, Claude Desktop. Read the repository before you give any tool access to your data.

npx agentmarket install connector/rasinmuhammed-misata

About

Use cases: testing, database-access

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What this server can do and what it asks for, from its tool list, install config and code. Checked Oct 25, 2026.

Tools

8 read · 3 write · 1 delete

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Tools (12)

Read from the source code; the running server may differ. Descriptions are the server’s own words; hidden characters are shown as [U+…].

• audit_datasetAudit a dataset for reader-visible contradictionsread-only

  Score a folder of CSVs for the contradictions a human reader would catch. This is Misata's coherence audit run on data that already exists — data an agent generated in an earlier step, data a user built by hand, or the output of some other tool. It checks, among other things: * timestamps that run backwards (shipped before ordered, resolved before opened), * derived columns that do not reconcile with their inputs (``total`` != ``quantity * unit_price``), * geographic fields that disagree (city / state / postcode / country), * near-constant columns (98% one value — a distribut…

• check_realismCheck a dataset for the tells of synthetic dataread-only

  Find the statistical shapes that make a dataset look generated. Where ``audit_dataset`` finds contradictions, this finds shapes real data almost never has, with no real data to compare against: money spread evenly from min to max, every customer with about the same number of orders, perfectly balanced categories, flat weekday or hour-of-day profiles, timestamps piled up at midnight, placeholder values (@example.com, John Doe), emails unrelated to names, small name pools, templated or repetitive free text, prose with a tiny vocabulary, review text whose tone ignores its rating, descriptions un…

• create_sandboxCreate an instant sandbox database

  Create and seed a real, isolated SQLite sandbox database for testing. Perfect for AI coding agents: spins up a queryable database with realistic, referentially intact tables (0 orphan foreign keys) so you can test SQL queries, analytics logic, or application code immediately without touching a real database. Args: domain: Pre-built domain template ('saas', 'ecommerce', 'fintech', 'healthcare'). story: Plain-English description (e.g. 'A restaurant with tables, bookings, and guests'). schema: Explicit schema dict if you want exact column control. rows: Base row count (default: …

• generate_datasetGenerate a dataset from a sentence

  Generate a synthetic dataset from a story and write it to disk as CSV files. Returns the output directory, file paths, row counts per table, and a small sample of rows for each table so the agent can show the user what was produced without loading every row into context. Args: story: Plain-English description of the dataset. rows: Default row count for the primary table. seed: Optional random seed (same seed → byte-identical output). output_dir: Where to write CSVs. Defaults to a fresh temp dir. sample_rows: Number of rows from each table to incl…

• generate_from_schemaGenerate a dataset from a schema

  Generate a dataset from a schema you design. This is the primary Misata tool. DIVISION OF LABOUR You (the agent) design what the data should look like — the tables, columns, business rules, and declared targets. Misata handles the hard guarantees: every FK resolves, every rollup reconciles to the cent, every declared outcome curve is hit exactly, every seed produces byte-identical output. The response includes a per-relationship integrity proof. SCHEMA FORMAT {table_name: {column_name: spec, ...}, ...} TABLE-LEVEL KEYS (inside a table dict) "__rows__": 5000 Per-table row count; …

• inspect_schemaInspect the schema behind a storyread-only

  Return the full schema (tables, columns, relationships) for a story without generating data. Heavier than ``preview_story`` — includes every column with its type and distribution params. Use when the user wants to see the structure they'll get, or to author a ``misata.yaml`` file from a natural-language seed. Args: story: Plain-English description of the dataset. rows: Default row count for the primary table.

• list_domainsList built-in domainsread-only

  List the 18 built-in business domains Misata can generate from natural language. Each domain has trigger keywords and a sample story you can pass to ``preview_story`` or ``generate_dataset``. Use this when the user asks "what kinds of data can you generate?" or to suggest a story format.

• preview_storyPreview how a story is interpretedread-only

  Inspect what Misata would generate from a story — without generating any rows. Returns the detected domain, confidence, near-misses, locale, scale, and a preview of the tables that would be produced. Use this to confirm interpretation before committing to a (potentially large) generation. Args: story: Plain-English description of the dataset. rows: Default row count for the primary table (affects preview only).

• query_sandboxQuery the sandbox databaseread-only

  Execute a read-only SQL query against the sandbox database. Allows AI coding agents to verify SQL queries, test aggregations, or inspect data in the sandbox directly without opening external terminals. Args: query: SQL SELECT query to execute. db_path: Optional path to the database (defaults to '.misata/sandbox.db'). limit: Max rows to return (default: 100). Returns: Dict with columns, rows (as list of dicts), row_count, and execution status.

• seed_databaseSeed a real databasecan change or delete data

  Fill a live Postgres or SQLite database with realistic, connected data, read from the database's own schema. Reads the tables, columns, and foreign keys directly from the target database, generates data that respects them, inserts parents before children, then queries the database back to confirm every foreign key resolves. No schema file and no ORM are needed: a connection string is enough. SAFETY — this is the only Misata tool that writes to a user's database: * It **plans by default**. With ``apply=False`` (the default) nothing is written; you get the table list, insert order, exist…

• validate_domainValidate a dataset against domain rulesread-only

  Flag values that are physiologically or financially impossible for a domain. Where ``audit_dataset`` checks internal consistency, this checks values against what the outside world allows. Built-in ranges include, for ``clinical`` / ``clinical_trial``: HbA1c 4-14%, BMI 10-80, systolic BP 60-260, age 0-130, glucose 2-40, cholesterol 1-20, hemoglobin 3-25; for ``financial`` / ``fintech``: price >= 0, discount 0-1, rate -1 to 100. Use after generating a dataset in a regulated or measurement-heavy domain, or when the user asks "is this data plausible for a real clinic / bank?". Args: dataset…

• validate_yamlValidate a misata.yamlread-only

  Validate a ``misata.yaml`` document at two levels. Runs both checks in sequence: 1. **Structural** — the published JSON Schema (correct field types, required fields, enum values). Catches typos and shape errors. 2. **Semantic** — ``misata.validate_schema`` (probabilities sum to 1.0, every foreign_key has a matching Relationship, no cycles, outcome curves reference real columns, etc.). These are the rules that would crash generation; the error messages include suggested fixes. Use this when an agent has authored or edited a misata.yaml on the user's behalf and wants to…

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1

Weekly downloads

264

Last commit

Oct 15, 2026 (16 days ago)

Last release

Oct 15, 2026 (16 days ago)

Last package publish

Oct 15, 2026 (16 days ago)

From GitHub (fetched Oct 24, 2026) and the package registry.

Details

Registry name

io.github.rasinmuhammed/misata

Version

0.9.6.60

Auth

No auth

Packages

pypi misata

License

MIT

Repository

github.com/rasinmuhammed/misata

Website

www.misata.studio/docs

First seen

Oct 24, 2026

Last checked

Oct 24, 2026

Source

Official MCP Registrygithub-graphql