> ## Documentation Index > Fetch the complete documentation index at: https://docs.elementary-data.com/llms.txt > Use this file to discover all available pages before exploring further. # Context Engine The Context Engine is the knowledge layer that powers the Elementary AI Agent and the MCP Server. It connects lineage across your entire stack — from your data warehouse and dbt models through to BI dashboards and code — and layers in everything Elementary knows about how your data is actually performing. The result is a unified, always-current picture of your stack: what exists, how it's connected, how it's running, and its health status. ## What it holds **From your connected integrations:** * **Data warehouse** — table metadata, schema definitions, query history, usage stats * **dbt** — model definitions, test configurations, run results, documentation * **BI tools** — dashboards, reports, and their upstream dependencies * **Code repository** — recent commits, pull requests, change history * **Orchestration** — job runs, execution history **From Elementary's own monitoring:** * **Test results** — pass/fail history across dbt, Python, and Cloud tests, coverage gaps * **Test performance** — execution times and cost per test run * **Model performance** — execution times, cost, trends over time * **Health scores** — data quality dimensions across all assets Lineage connects these layers. A table in your warehouse maps to a dbt model, which feeds a dashboard, which is owned by a team. When something breaks, the Context Engine already knows the blast radius. ## What it enables Because the Context Engine holds a complete, connected view of your stack, the Elementary AI Agent can: * Trace the root cause of an incident across models, code changes, and upstream dependencies * Understand the downstream impact before escalating or fixing * Identify coverage gaps and recommend tests for the right assets * Understand which tests are redundant and remove them to speed up the pipeline * Enrich metadata with descriptions that reflect how assets are actually used The MCP Server exposes this same context to external AI tools — Claude, Cursor, or any MCP-compatible client — so they can query your stack with full awareness. The more integrations you connect, the more complete the context. Lineage between dbt and BI tools, for example, only works when both are connected. Add data sources to expand what the Context Engine can see. Expose the Context Engine to external AI tools.