## Automate metadata operations

URL: https://docs.atlan.com/product/capabilities/atlan-ai/references/agent-automation-use-cases

> Patterns and examples for automating Atlan metadata operations using custom Python agents, LangChain pipelines, and n8n workflows.

# Automate metadata operations

Atlan MCP exposes every catalog operation as a callable tool—which means any automation platform can invoke them directly. Use these patterns to run governance sweeps, lineage impact analyses, glossary bootstrapping, stale asset reports, and data quality sweeps from Python, n8n, LangChain, or any agent framework.

Unlike conversational use cases where a person types a prompt, these patterns run programmatically—on a schedule, triggered by pipeline events, or as part of a broader data platform workflow. Each pattern shows the exact tool calls, the order they run in, and ready-to-use code.

:::info
Make sure the Atlan MCP server is running and your agent is connected before using any of these patterns. For setup, see [Set up Atlan MCP](https://docs.atlan.com/llms/governance/atlan-mcp-overview/llms.txt).
:::

## Governance sweep

## Lineage impact analysis

## Business graph bootstrap

## Stale asset report

For n8n MCP Client setup, see [Set up n8n with Atlan MCP](https://docs.atlan.com/llms/governance/atlan-mcp-overview/remote-mcp-overview/llms.txt).

## Data quality sweep

/DB/SCHEMA/ORDERS"
 }
 },
 "limit": 100
})

for col in columns["results"]:
 rule = await client.call_tool("create_dq_rules_tool", {
 "rule_type": "Null Count",
 "asset_qualified_name": col["table_qualified_name"],
 "column_qualified_name": col["qualified_name"],
 "threshold_value": 0,
 "threshold_compare_operator": "EQUAL",
 "alert_priority": "HIGH"
 })

 await client.call_tool("schedule_dq_rules_tool", {
 "rule_id": rule["id"],
 "cron": "0 2 * * *"
 })`}
/>

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