Scherlok MCP server

<!-- mcp-name: io.github.rbmuller/scherlok -->

Scherlok

Zero-config anomaly detection for your database tables. No YAML, no rules, no thresholds. Scherlok learns what "normal" looks like, then tells you when something changes.

pip install scherlok
scherlok ci postgres://user:pass@host/db   # profiles on the first run, detects anomalies on every run after

No database handy? The demo seeds one, learns it, breaks it, and catches it, in about a second:

uvx --from "scherlok[duckdb]" scherlok demo

Works with PostgreSQL, BigQuery, Snowflake, MySQL, DuckDB and dbt. Alerts go to Slack, Discord, Teams, email, or your CI exit code.

The Problem

Every data team has the same nightmare:

A source API silently changes from dollars to cents. Revenue dashboards show wrong numbers for 3 weeks before anyone notices.

A column starts returning NULLs. A table stops updating. Row counts drop 40% on a Tuesday. Nobody knows until the CEO asks why the report looks weird.

Current tools (Great Expectations, Soda, dbt tests) require you to define what "correct" looks like before you can detect what's wrong. Hundreds of rules. Dozens of YAML files. And you still miss things — because you can't write rules for problems you haven't imagined yet.

What It Catches

Every anomaly is auto-scored: INFO, WARNING, or CRITICAL. No thresholds to configure.

How It Works

Scherlok takes the opposite approach of rule-based tools: learn first, then detect.

scherlok connect postgres://user:pass@host/db   # connect once
scherlok investigate                              # learn your data
scherlok watch                                    # detect anomalies

Three commands. Five minutes. Done. (scherlok ci runs all three in one step for pipelines.)

Shortened. The full README is on GitHub.