Databricks MCP Server
A Model Completion Protocol (MCP) server for Databricks that provides access to Databricks functionality via the MCP protocol. This allows LLM-powered tools to interact with Databricks clusters, jobs, notebooks, and more.
Features
- MCP Protocol Support: Implements the MCP protocol to allow LLMs to interact with Databricks
- Databricks API Integration: Provides access to Databricks REST API functionality
- Tool Registration: Exposes Databricks functionality as MCP tools
- Async Support: Built with asyncio for efficient operation
Available Tools
The Databricks MCP Server exposes the following tools:
- list_clusters: List all Databricks clusters
- create_cluster: Create a new Databricks cluster
- terminate_cluster: Terminate a Databricks cluster
- get_cluster: Get information about a specific Databricks cluster
- start_cluster: Start a terminated Databricks cluster
- list_jobs: List all Databricks jobs
- run_job: Run a Databricks job
- list_notebooks: List notebooks in a workspace directory
- export_notebook: Export a notebook from the workspace
- list_files: List files and directories in a DBFS path
- execute_sql: Execute a SQL statement
Installation
Prerequisites
- Python 3.10 or higher
uvpackage manager (recommended for MCP servers)
Setup
- Install
uvif you don't have it already:Restart your terminal after installation.
- Clone the repository:
- Set up the project with
uv:
- Set up environment variables:You can also create an
.envfile based on the.env.exampletemplate.
Running the MCP Server
To start the MCP server, run:
These wrapper scripts will execute the actual server scripts located in the
scripts directory. The server will start and be ready to accept MCP protocol connections.You can also directly run the server scripts from the scripts directory:
Querying Databricks Resources
The repository includes utility scripts to quickly view Databricks resources:
Project Structure
See
project_structure.md for a more detailed view of the project structure.Development
Code Standards
- Python code follows PEP 8 style guide with a maximum line length of 100 characters
- Use 4 spaces for indentation (no tabs)
- Use double quotes for strings
- All classes, methods, and functions should have Google-style docstrings
- Type hints are required for all code except tests
Linting
The project uses the following linting tools:
Testing
The project uses pytest for testing. To run the tests:
You can also run the tests directly with pytest:
A minimum code coverage of 80% is the goal for the project.
Documentation
- API documentation is generated using Sphinx and can be found in the
docs/apidirectory
- All code includes Google-style docstrings
- See the
examples/directory for usage examples
Examples
Check the
examples/ directory for usage examples. To run examples:Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
- Ensure your code follows the project's coding standards
- Add tests for any new functionality
- Update documentation as necessary
- Verify all tests pass before submitting
License
This project is licensed under the MIT License - see the LICENSE file for details.
Databricks MCP Server
A Model Completion Protocol (MCP) server for Databricks that provides access to Databricks functionality via the MCP protocol. This allows LLM-powered tools to interact with Databricks clusters, jobs, notebooks, and more.
Features
- MCP Protocol Support: Implements the MCP protocol to allow LLMs to interact with Databricks
- Databricks API Integration: Provides access to Databricks REST API functionality
- Tool Registration: Exposes Databricks functionality as MCP tools
- Async Support: Built with asyncio for efficient operation
Available Tools
The Databricks MCP Server exposes the following tools:
- list_clusters: List all Databricks clusters
- create_cluster: Create a new Databricks cluster
- terminate_cluster: Terminate a Databricks cluster
- get_cluster: Get information about a specific Databricks cluster
- start_cluster: Start a terminated Databricks cluster
- list_jobs: List all Databricks jobs
- run_job: Run a Databricks job
- list_notebooks: List notebooks in a workspace directory
- export_notebook: Export a notebook from the workspace
- list_files: List files and directories in a DBFS path
- execute_sql: Execute a SQL statement
Installation
Prerequisites
- Python 3.10 or higher
uvpackage manager (recommended for MCP servers)
Setup
- Install
uvif you don't have it already:Restart your terminal after installation.
- Clone the repository:
- Set up the project with
uv:
- Set up environment variables:You can also create an
.envfile based on the.env.exampletemplate.
Running the MCP Server
To start the MCP server, run:
These wrapper scripts will execute the actual server scripts located in the
scripts directory. The server will start and be ready to accept MCP protocol connections.You can also directly run the server scripts from the scripts directory:
Querying Databricks Resources
The repository includes utility scripts to quickly view Databricks resources:
Project Structure
See
project_structure.md for a more detailed view of the project structure.Development
Code Standards
- Python code follows PEP 8 style guide with a maximum line length of 100 characters
- Use 4 spaces for indentation (no tabs)
- Use double quotes for strings
- All classes, methods, and functions should have Google-style docstrings
- Type hints are required for all code except tests
Linting
The project uses the following linting tools:
Testing
The project uses pytest for testing. To run the tests:
You can also run the tests directly with pytest:
A minimum code coverage of 80% is the goal for the project.
Documentation
- API documentation is generated using Sphinx and can be found in the
docs/apidirectory
- All code includes Google-style docstrings
- See the
examples/directory for usage examples
Examples
Check the
examples/ directory for usage examples. To run examples:Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
- Ensure your code follows the project's coding standards
- Add tests for any new functionality
- Update documentation as necessary
- Verify all tests pass before submitting
License
This project is licensed under the MIT License - see the LICENSE file for details.