Agentic data quality MCP server — runs structured validation rules against warehouses (DuckDB, BigQuery, Athena, Databricks, Postgres), diagnoses failures with LLM root cause analysis, and proposes SQL remediations. Full audit trail of every AI decision.
Automatically discovers database schema, performs data quality checks on tables and columns, and generates natural-language root cause analysis reports using Ollama LLM.
Enables LLM-driven agents to autonomously detect, diagnose, repair, verify, and prevent software and hardware failures on local and remote systems. Includes built-in safety checks and automatic rollbacks.
Zero-config data quality monitoring as MCP tools. Profiles a warehouse (Postgres, BigQuery, Snowflake, MySQL, DuckDB), detects anomalies, and gates CI — read-only with the connection resolved server-side, never via the model.
AI-driven MCP server that audits, profiles, detects schema drift, and auto-generates documentation for dbt projects, enabling natural language interaction with your dbt project's health.
Enables LLMs to propose UPDATE/DELETE SQL that is run in a transaction, measured, and rolled back, requiring human approval before applying to prevent unauthorized changes.
