MCP工具

Seed it back: insert order respects FK dependencies automatically

report = seed_database(tables, "postgresql://user:pass@localhost/myapp_dev")

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)

---

format="pdf" requires: pip install "misata[documents]"

Quick start

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

Mine a capsule from example data you already have: no LLM, no key

misata capsule create --domain veterinary --from-csv ./samples/ -o vet.capsule.json misata capsule show vet.capsule.json

./demo_data/oracle_report.json

1. Plain English, no config required

tables = misata.generate("A fintech startup with 10k customers, fraud rate 3%, and IBAN accounts")

Misata reads the story, infers domain (fintech), scale (10 000 rows), and column semantics (fraud flag, IBAN format), no schema authoring needed.

5. LLM-assisted generation, richer semantics, optional

```python from misata import LLMSchemaGenerator

gen = LLMSchemaGenerator(provider="groq", model="llama-3.3-70b-versatile") # free tier, fast & reliable

gen = LLMSchemaGenerator(provider="ollama", model="llama3") # fully local, no API key

schema = gen.generate_from_story( "A fraud detection dataset, 2% positive rate, FICO scores, transaction velocity features" ) tables = misata.generate_from_schema(schema) ```

Requires pip install "misata[llm]" plus one of GROQ_API_KEY, OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY.

Groq model tip: llama-3.3-70b-versatile is the reliable free-tier default. Larger models (e.g. openai/gpt-oss-120b) can return 413 Request too large on Groq's free tier, so use them only on a paid tier. Whatever the model returns, generation never crashes on an imperfect schema: missing relationships, malformed probabilities, and out-of-range time_units are repaired automatically.

inspections: inspection_id, beekeeper_id, apiary_id, hive_id, inspection_date, status

One-command workflow

misata init --db postgresql://user:pass@localhost/myapp # writes misata.yaml misata generate --db-url postgresql://user:pass@localhost/myapp_dev --db-create

SQLAlchemy models are supported too:

report = seed_from_sqlalchemy_models(Base, db_url="sqlite:///test.db", row_count=500, create_tables=True) ```

Data validation: catch out-of-bounds values before they reach your pipeline

After generation, validate against declared domain bounds before the data reaches a model or a dashboard:

```python tables = misata.generate_from_schema(schema)

report = misata.validate_domain(tables, domain="financial") print(report.summary())