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Run, test, and deploy code on Databricks clusters using natural language.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Databricks Code Execution MCP" yet — see the docs or source repo.
Run this PySpark code on a Databricks cluster, identify and fix any errors, then return the final executable version and a summary of test results.
Returns corrected code, error analysis, execution results, and test pass status.
Execute this SQL in Databricks, verify whether the metric logic is correct, and provide optimization suggestions plus a revised query.
Returns validation findings, performance recommendations, and an improved SQL query.
Review this Databricks Asset Bundle configuration, fix any pre-deployment issues, deploy it to the target environment, and summarize the deployment steps and results.
Returns the corrected configuration, deployment outcome, and an environment release summary.
Manage Databricks clusters, jobs, SQL, and catalogs through MCP tools.
Search and explore Databricks AWS documentation with semantic search tools.
Explore Databricks metadata, run SQL, and analyze lineage for data discovery.
Run Python code securely with session state and file access in containers.
Run stateful code, query SQL, and manage files for development workflows.
Build and deploy a production-ready MCP server for Databricks Apps.