It exposes Databricks functionality as MCP tools, allowing LLMs to trigger actions like creating clusters, running jobs, or listing notebooks.

What can I do with it?

You can automate Databricks tasks, build LLM-powered data science workflows, and integrate Databricks resources into your AI applications.

What Databricks resources can I access?

You can access clusters (listing, creating, terminating, starting), jobs (listing, running), notebooks (listing, exporting), files in DBFS, and execute SQL statements.

What are the prerequisites for installation?

You need Python 3.10 or higher and the uv package manager (recommended for MCP servers).

Databricks

Name: Databricks
Author: JustTryAI

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Enables LLMs to interact with Databricks clusters, jobs, notebooks, and more via the Model Completion Protocol (MCP).

Databricks provides a Model Completion Protocol (MCP) server that allows Large Language Models (LLMs) to seamlessly interact with Databricks services. By exposing Databricks API functionality as MCP tools, LLMs can manage clusters, run jobs, interact with notebooks, and access DBFS, extending their capabilities within the Databricks environment. This server facilitates automation and orchestration tasks through natural language interfaces, empowering users to leverage the power of LLMs for Databricks workflows.

主要功能

01Implements the MCP protocol for LLM interaction with Databricks.

02Integrates with the Databricks REST API to provide access to Databricks functionality.

03Exposes Databricks functionality as MCP tools.

04Supports asynchronous operations for efficient performance.

05Provides tools for listing, creating, terminating, and starting Databricks clusters.

064 GitHub stars

使用案例

01Automating Databricks cluster management using natural language commands.

02Querying and manipulating data within DBFS through LLM interfaces.

03Running Databricks jobs and notebooks via LLM-driven orchestration.