Justtryai Databricks MCP Server
The Databricks MCP Server is a service that implements the Model Completion Protocol (MCP), providing the ability to access Databricks functions via the MCP protocol, enabling LLM tools to interact with Databricks clusters, jobs, notebooks, etc.
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What is the Databricks MCP Server?
This is a bridge server that connects AI tools with the Databricks data platform. It uses the Model Completion Protocol (MCP) to allow language models to directly operate computing resources, data jobs, and notebook files on Databricks.How to use the Databricks MCP Server?
Simply configure the Databricks access credentials. After starting the server, your AI application can send instructions via the standard MCP protocol to manage Databricks resources.Use cases
Suitable for data teams that need to automate the management of Databricks resources, AI-assisted development scenarios, and the construction of intelligent applications integrated with Databricks.Main features
Cluster managementCreate, view, start, and terminate Databricks computing clusters
Job controlRun and monitor the execution status of data jobs
Notebook operationsBrowse workspace notebooks and export notebook contents
SQL executionRun SQL queries directly on the connected cluster
Advantages and limitations
Advantages
Standardized interface: Provides a unified AI interaction method via the MCP protocol
Comprehensive coverage: Supports most operations of Databricks core functions
Asynchronous and efficient: Implements high-performance concurrent processing based on asyncio
Limitations
Requires a Python 3.10+ environment
Databricks access credentials need to be configured for the first use
Some advanced functions require enterprise-level Databricks permissions
How to use
Installation preparation
Ensure that Python 3.10 or a higher version is installed. It is recommended to use the uv package manager.
Get the code
Clone the GitHub repository to the local machine
Environment configuration
Set the Databricks access credentials
Start the service
Run the startup script
Usage examples
Automated cluster managementAutomatically create and adjust the cluster scale according to the workload
Intelligent job schedulingLet AI automatically arrange the job execution order according to the data dependency relationship
Frequently asked questions
What kind of Databricks permissions are required?
Which versions of Databricks are supported?
How to ensure communication security?
Related resources
MCP protocol specification
Official documentation of the Model Completion Protocol
Databricks API reference
Official Databricks REST API documentation
GitHub repository
Project source code and issue tracking
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