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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.
Search and explore Databricks AWS documentation with semantic search tools.
Semantically search Databricks docs, APIs, Terraform references, and knowledge base content.
Ask data questions, run SQL, and chat with Databricks Genie agents.
Safely explore, profile, and query data with read-only SQL over MCP.
A persistent DuckDB MCP server lets AI assistants query CSV, Parquet, and cloud data in natural language.
Build and deploy a production-ready MCP server for Databricks Apps.