# Databricks MCP server

Source: https://agentic-cmms.klementine.xyz/integrations/databricks

> Connect Databricks to Claude, ChatGPT and other AI agents to query Delta tables, failure predictions and maintenance KPIs from your lakehouse in plain English.

Databricks is the lakehouse platform that many engineering-led companies use to bring sensor data, maintenance history and ERP data together, then build models on top of it. Reliability and data science teams use it to train failure prediction models, clean historian data and publish curated Delta tables for reporting.

The people who could act on that work are often the furthest from it. A maintenance supervisor will not open a notebook to see which motors the model flagged this week. Agentic CMMS puts an AI agent between them: the supervisor asks a plain question, and the agent queries the lakehouse through a SQL warehouse and explains the answer in maintenance terms.

## What your AI agent can do with Databricks

- **Check the risk list.** "Which assets have the highest predicted failure risk this week?" The agent reads your scores table and ranks the assets with their site.
- **Explain a prediction.** "Why is Motor M-204 flagged?" It reads the features stored with the score, such as rising vibration or temperature, and explains them.
- **Calculate KPIs.** "What is MTBF and MTTR per asset class across all plants this year?" The agent queries your curated work order tables.
- **Track the PM programme.** "Has the planned work share improved since the new PM routes started in June?"
- **Measure backlog health.** "How much of our backlog is older than 30 days, by plant and priority?"
- **Explore the catalog.** "Which tables in the reliability schema hold historian data, and how fresh are they?"
- **Check a pipeline.** "Did last night's job that loads work orders from the CMMS succeed?" With write access, the agent can rerun a job your admins allow.

## Databricks with your other systems

- When the model flags a motor, ask the agent to check its latest readings in [Siemens Insights Hub](/integrations/siemens-insights-hub) or the vibration alerts in [Augury](/integrations/augury).
- Turn the top of the risk list into planned work orders in [IBM Maximo](/integrations/ibm-maximo), each with the prediction attached as the reason.
- Fit the planned repairs into the next shutdown schedule in [Oracle Primavera P6](/integrations/oracle-primavera-p6).
- Compare the lakehouse KPIs with the published figures on your [Tableau](/integrations/tableau) reliability dashboard.

## From prediction to work order

A failure prediction only saves money when it turns into a job. The usual gap between a data science team and a maintenance team is a spreadsheet emailed every Friday. With an agent that reads Databricks and your CMMS in the same conversation, a supervisor goes from "this motor is at risk" to "the inspection is scheduled" in a few minutes.

Each person approves their own agent with their own login. Owners and admins choose which roles may query or rerun jobs, and every action is recorded in the activity log.

## Questions and answers

### Can Claude query Delta tables in Databricks?

Yes. The agent runs SQL through a Databricks SQL warehouse on the catalogs and schemas your connection can read, and explains the result.

### Can an AI agent read our failure prediction scores in Databricks?

Yes, if your data team writes the scores to a table. The agent can list the assets with the highest risk and explain which readings drove each score.

### Does the agent follow our Unity Catalog permissions?

It works through the identity behind your Databricks connection, so it sees only what that identity is granted. Agentic CMMS adds per-role tool permissions on top.