databricks · MCP Server

Connect Databricks to AI with Improvado MCP

Improvado's MCP server connects Databricks to Claude, Cursor, and other AI agents. Query your Databricks data in natural language — no manual exports or API scripts required.

46K+ metrics ·Read & Write access ·500+ platforms ·<60s setup

Read

Read: Instant Answers from Databricks

Stop writing ad-hoc SQL and waiting for notebook runs. Ask your AI agent to query Unity Catalog tables, explore Delta Lake schemas, surface data quality issues, and pull business metrics — across any catalog, schema, or workspace.

Example prompts

"Show me the top 10 revenue-generating accounts from the customer_transactions table for Q1, with month-over-month growth."

30 min → 1 min

"Which columns in the marketing_attribution schema have null rates above 20%? List them with the field name, null count, and total row count."

45 min → 2 min

"Query the ad_spend_harmonized table and show me any campaign records where LinkedIn campaign name or ad name is null — with the source connector and date range."

25 min → 1 min

Works with Claude ChatGPT Cursor +5

Write

Write: Automate Databricks Operations

Go beyond querying. Your AI agent can trigger pipeline runs, update table properties, create jobs, and modify Unity Catalog metadata — from a single prompt, without opening the Databricks UI.

Example prompts

"Trigger a full refresh of the marketing_attribution pipeline and notify me when it completes with row counts."

15 min → 1 min

"Add a data quality tag to the ad_spend_raw table indicating that LinkedIn campaign name fields have known null issues in records before 2024-01-01."

20 min → 2 min

"Create a new Delta table in the staging schema with this schema definition and insert the first batch of records."

40 min → 3 min

Every action logged · Fully reversible · SOC 2 certified

Monitor

Monitor: Catch Databricks Pipeline Issues Before They Reach Dashboards

Set AI-powered watches on pipeline health, data quality metrics, and table freshness. Get proactive alerts when row counts drop, null rates spike, or jobs fail — before downstream reports are impacted.

Example prompts

"Alert me if any table in the marketing_analytics catalog hasn't been updated in more than 24 hours during a business day."

Manual → auto

"Every morning: send a summary of all Databricks job runs from the last 24 hours — successes, failures, and duration outliers."

2 hrs → auto

"Flag if the null rate in the linkedin_campaign_name column of ad_spend_harmonized increases by more than 5% compared to the 7-day baseline."

Manual → auto

Alerts sent to Slack, email, or your AI agent

Full cycle

The Closed Loop: Read → Decide → Write → Monitor

Your AI agent doesn't just surface data — it acts. Adjust pricing, update product descriptions, manage inventory, apply discounts — all through natural language. The MCP server translates intent into API operations.

Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.

Ideate

Launch

Measure

Analyze

Report

Iterate

One conversation. All six phases. Every platform.

The daily grind

Common problems. Direct answers.

Challenge 1

Data Harmonization Issues Are Hard to Diagnose

The problem

When custom field mappings and standard Improvado conversions both write to the same Databricks destination, fields don't appear as expected. Diagnosing whether the issue is in the extraction, transformation, or load layer requires querying multiple tables and tracing lineage — a task that can consume an entire day.

How MCP solves it

Ask your AI agent to trace a specific field through the pipeline layers: raw → staged → harmonized. It identifies where the value drops off or gets overwritten, and returns the root cause with the relevant table and column.

Try asking

Trace the 'campaign_type' field for the Google Ads connector through raw, staged, and harmonized tables. At which layer does the value go null, and what transformation caused it?

Answer in seconds

All data sources, one query

Challenge 2

Null Campaign and Ad Names Break Attribution Models

The problem

When LinkedIn campaign names or ad names arrive as null in the Databricks destination, attribution models silently misattribute spend. Identifying the scope — which accounts, which date ranges, which connectors — requires querying raw and harmonized tables and joining them with account metadata.

How MCP solves it

Ask your AI agent to quantify the null name issue across all accounts and date ranges in one prompt. It surfaces the affected records, scopes the impact on attribution, and identifies whether the issue is upstream (connector) or downstream (transformation).

Try asking

In the ad_spend_harmonized table, show me the count and percentage of records where linkedin_campaign_name or linkedin_ad_name is null, grouped by account and month for the last 6 months.

Full detail preserved

No data loss on export

Challenge 3

POC Validation Across Databricks and Azure Requires Parallel Access

The problem

Running a proof-of-concept that spans Databricks and Azure services means querying both environments, comparing schemas, validating data consistency, and documenting findings — typically across multiple tools and windows. The coordination overhead slows down POC cycles significantly.

How MCP solves it

Ask your AI agent to query both Databricks and Azure data sources in the same session. It validates schema alignment, checks row count parity, and produces a POC readiness report — without switching tools or environments.

Try asking

Compare the row counts and schema for the 'events' table between our Databricks staging environment and the Azure Blob staging export from the same date range. Flag any discrepancies.

Unified data model

Compare anything side by side

👥 Teams

One Framework. Five Roles. Zero Setup.

Same MCP connection, different workflows for every team member. Each role asks in natural language — the MCP server handles the complexity (rate limits, auth, schema normalization, governance) behind the scenes.

Agency CEO

Portfolio health. Client risk. Revenue signals.

Media Strategist

70% strategy, not 70% ops. Auto campaign QA.

Marketing Analyst

Zero wrangling. Cross-platform. AI narratives.

Account Manager

QBR decks auto-generated. Call prep in 30s.

Creative Director

Performance-to-brief. Predict winners before spend.

FAQ

Common questions

What is Databricks MCP?

Databricks MCP is a Model Context Protocol server that connects your Databricks lakehouse — including Unity Catalog, Delta Lake tables, jobs, and pipelines — to AI agents like Claude, ChatGPT, and Gemini. It lets you query and manage Databricks in natural language — all through Improvado's hosted MCP server.

Which Databricks resources can I access through the MCP server?

Unity Catalog tables and schemas, Delta Lake data, Databricks SQL warehouses, job runs and pipeline statuses, cluster metadata, and workspace configuration. Queries execute against your existing Databricks SQL warehouse.

Can the AI agent run write operations or only queries?

Both. Read operations cover querying tables, exploring schemas, and checking job statuses. Write operations include triggering job runs, creating and updating tables, modifying Unity Catalog tags and properties, and inserting data. All operations require appropriate Databricks service principal permissions.

How does the MCP server connect to Databricks — does it use my existing cluster?

Improvado connects via Databricks SQL warehouse using your workspace URL, personal access token or service principal credentials. Queries run on your existing warehouse — no additional compute is provisioned by Improvado.

Is my Databricks data secure through the MCP server?

Yes. Improvado stores all Databricks credentials in an encrypted vault certified to SOC 2 Type II. The AI model never has direct access to your lakehouse — requests are proxied through Improvado's secure layer with prompt injection protection.

How quickly can I set this up?

Under 5 minutes. Provide your Databricks workspace URL and access token, add the MCP server URL to your config, and start querying. No infrastructure changes required on your Databricks side — all through Improvado's hosted MCP server.

Stop Reporting. Start Executing.

Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.

SOC 2 Type II GDPR 500+ Platforms