Databricks MCP Server for Synthetic Data Generation with dbldatagen
Wellknown found it in public sources; nobody has proven control of it yet. Claiming takes one click if the repository is under your GitHub account, or a small file on your domain otherwise. Verified owners get the badge, 15-minute checks, status alerts, edits that outrank crawled data, and a ranking boost.
Agents can do it too: POST https://wellknown.network/api/v1/claims with {"agent":"databricks-dbldatagen-mcp","method":"well_known_file"} — machine-readable steps at claim.json, guide at /docs/claim.
Everything here was measured by our prober or read from a registry. Nothing is self-reported.
Attributed to the source that supplied each field. Treated as claims, not facts.
# Databricks dbldatagen MCP Server A Model Context Protocol (MCP) server for generating synthetic test data using [dbldatagen](https://github.com/databrickslabs/dbldatagen) on Databricks. Enables AI assistants to analyze source tables, generate realistic synthetic data, run SQL queries, and manage notebooks — all through natural language. ## Features - **Schema Analysis** — Inspect column types, nullable flags, metadata, and detect primary keys - **Data Profiling** — Deep profiling including distributions, cardinality, null ratios, and pattern detection - **Synthetic Data Generation** — Content-aware generation using dbldatagen DataAnalyzer with preserved columns, fixed values, and schema casting - **SQL Execution** — Run any SQL query on Databricks (SELECT, DESCRIBE, CREATE, etc.) - **Notebook Operations** — Import, sync, and export notebooks to/from Databricks workspace - **Windows Support** — Full Windows compatibility with optimized async handling ## Architecture ``` ┌────────────────────────────────────────────────────────────┐ │ AI Assistant (VS Code) │ └───────────────────────────┬────────────────────────────────┘ │ MCP Protocol (stdio) ▼ ┌────────────────────────────────────────────────────────────┐ │ databricks-dbldatagen-mcp (FastMCP) │ │ │ │ tools/generate_data.py ────┐ │ │ tools/analyze_schema.py ───┤ │ │ tools/profile.py ──────────┼──► @mcp.tool decorators │ │ tools/sql.py ──────────────┤ │ │ tools/notebook_ops.py ─────┘ │ │ │ │ core/analyzer.py ──────────── DataProfiler │ │ auth.py ───────────────────── Authentication & caching │ │ identity.py ───────────────── User-agent t…
Mapped onto the structured taxonomy from declared text and observed tool names. Confidence shown for derived entries.
Every source is kept verbatim. Field changes are logged as events.