About this demo
The slowest part of a new pipeline isn't the code. It's figuring out which tables to start with, confirming downstream impact, and stitching that context into your work before you write a single line. With the Atlan MCP connected to your AI coding agent, all of that happens inside the IDE. Hand the agent a plain business ask—"build an Airflow DAG for sales order analytics"—and it doesn't just return table names. It returns a ranked set with rationale, identifying the spine of the analysis and a suggested build sequence, with Atlan's metadata doing the work: descriptions, schema, context, and classification all factor in. Pull the full column-level picture, then run impact analysis right in chat—no switching to Atlan, no opening lineage manually. The agent flags downstream dependencies and tells you which teams to consult before touching anything. Then it generates a DAG scaffold grounded in your real metadata, not a generic template—so you can take a structure shaped by the actual data state back to stakeholders before a single table is touched. No context switching. Discovery, impact analysis, and scaffolding all live where the engineer is already working. The MCP supplies the metadata layer; your judgment stays active throughout. What used to take the better part of a week happens in hours—with less risk of shipping something that breaks downstream. The same MCP-connected approach extends to existing pipelines: ask what's downstream of any asset before you change it, and get the same lineage traversal without ever opening the UI.