Improvado MCP extracts data from dbt and makes it queryable by any AI agent. Ask about model performance, test failures, and lineage without opening a single dashboard.
Improvado MCP connects your dbt data to AI, so teams can query model run history, test results, freshness checks, and lineage metadata in plain English — no SQL, no manual exports.
"Which models failed tests in the last 7 days?"
30 min → 15 sec"Show me all models with stale data warnings"
Manual → auto"What's the run time trend for our mart models?"
1 hr → 1 minTrigger dbt runs, update model configurations, and manage documentation directly from your AI agent — without switching context or opening dbt Cloud manually.
"Run all models downstream of dim_customers"
5 min → 30 sec"Update materialization to incremental for slow models"
Manual → auto"Generate documentation for all staging models"
2 hrs → 3 minMonitor test failures, run time anomalies, and freshness violations automatically — your AI agent surfaces what matters before it impacts downstream users.
"Alert me if any model run time doubles"
Daily manual → auto"Which models haven't run successfully in 48 hours?"
Weekly report → instant"Track freshness check failures across all sources"
Manual → autoYour AI agent doesn't just surface data — it acts. Adjust pricing, update product descriptions, manage inventory, apply discounts — all through natural language. The MCP server translates intent into API operations.
Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.
One conversation. All six phases. Every platform.
Teams spend hours pulling dbt logs and metadata manually to understand model health and pipeline performance.
Improvado MCP extracts dbt data and makes it instantly queryable via AI — no manual exports needed.
Tracing data lineage and understanding downstream impacts requires navigating complex DAGs and manual documentation review.
AI agents query lineage metadata directly and surface upstream dependencies and downstream impacts in seconds.
Test failures, stale data, and run time spikes go unnoticed until they cause downstream reporting issues.
Continuous monitoring surfaces anomalies automatically — teams get alerts before issues escalate.
Same MCP connection, different workflows for every team member. Each role asks in natural language — the MCP server handles the complexity (rate limits, auth, schema normalization, governance) behind the scenes.
Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.
