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.
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.
Local-first MCP server for credit risk analysis. It enables explainable rule discovery, statistical validation, PSI drift monitoring, and root-cause diagnosis on user-approved Parquet data while preserving privacy.
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.
Local-first production-readiness MCP server for AI-built apps. It runs read-only checks, produces an evidence-based readiness score, and guides fixes before launch.
MCP server with 32 tools for ETL ingestion, AI-generated data quality rules, AI
transformations, vector search, and natural-language SQL. Works across Postgres,
MongoDB, Kafka, S3/MinIO, HashiCorp Vault, and five vector stores
(Qdrant, Weaviate, Milvus, Chroma, pgvector).
