MCP Data Observability Integration

Overview

Agents that cannot see production signals guess at fixes — scaling clusters blindly, rerunning jobs that OOM for structural reasons, or changing Kafka consumers without checking lag. This skill pairs MCP server templates with safe read-only observability workflows so diagnosis precedes mutation.

When to Use

• diagnosing Spark OOM, shuffle skew, or stage failures
• inspecting Kafka consumer group lag before replay or consumer changes
• reviewing Airflow DAG run state, task duration drift, or failed retries
• validating warehouse publish state before reopening downstream consumers
• setting up IDE-integrated observability for data engineering agents

Do not use MCP write capabilities for destructive fixes during incident triage unless explicitly approved and scoped.

Workflow