Provides seamless integration with Databricks Unity Catalog, enabling browsing of catalogs, schemas, and tables, querying metadata, sampling data, executing SQL queries against Databricks warehouses, searching tables, and accessing data quality insights and lineage information.
Databricks MCP Server
A Model Context Protocol (MCP) server that provides seamless integration with Databricks Unity Catalog. This server enables AI assistants to interact with your Databricks workspace, query metadata, sample data, and perform various Unity Catalog operations.
Features
- Unity Catalog Integration: Browse catalogs, schemas, and tables
- Metadata Querying: Get detailed information about tables, columns, and properties
- Data Sampling: Sample data from tables for analysis
- SQL Query Execution: Run SQL queries against your Databricks warehouses
- Table Search: Search for tables by name or metadata
- Data Discovery: Advanced search and filtering capabilities
- Data Quality Insights: Basic data quality analysis
- Lineage Information: Table lineage tracking (when available)
Installation
Prerequisites
- Python 3.8 or higher
- Databricks workspace access
- Databricks personal access token
Install from Source
Install Development Dependencies
Configuration
Environment Variables
Set the following environment variables:
Configuration File
Alternatively, create a config.json
file:
Usage
Running the Server
MCP Client Integration
The server implements the Model Context Protocol and can be used with any MCP-compatible client. Here's an example configuration for Claude Desktop:
Available Tools
Catalog Operations
list_catalogs
: List all Unity Catalog catalogslist_schemas
: List schemas in a cataloglist_tables
: List tables in a schema
Table Operations
describe_table
: Get detailed table information including columns and metadatasample_table
: Sample data from a table (configurable limit)search_tables
: Search for tables by name or metadata
Query Operations
execute_query
: Execute SQL queries against Databricks warehousesget_table_lineage
: Get lineage information for tables
Resources
The server exposes Databricks resources through URIs:
databricks://catalog/{catalog_name}
: Catalog informationdatabricks://catalog/{catalog_name}/{schema_name}
: Schema informationdatabricks://catalog/{catalog_name}/{schema_name}/{table_name}
: Table information
Examples
Basic Usage
Advanced Data Discovery
Development
Running Tests
Code Formatting
Type Checking
Troubleshooting
Common Issues
- Authentication Error: Verify your
DATABRICKS_TOKEN
is valid and has appropriate permissions - Connection Error: Check that
DATABRICKS_HOST
is correct and accessible - No Warehouses: Ensure you have at least one SQL warehouse running in your workspace
Debugging
Enable debug logging:
Configuration Validation
Use the built-in validation:
Security Considerations
- Never commit access tokens to version control
- Use environment variables or secure configuration management
- Limit token permissions to minimum required scope
- Consider using service principals for production deployments
Contributing
- Fork the repository
- Create a feature branch
- Make your changes
- Add tests
- Run the test suite
- Submit a pull request
License
MIT License - see LICENSE file for details.
Support
For issues and questions:
- Check the troubleshooting section
- Search existing issues
- Create a new issue with detailed information
Changelog
v0.1.0
- Initial release
- Basic Unity Catalog integration
- Table metadata and sampling
- SQL query execution
- MCP server implementation
This server cannot be installed
remote-capable server
The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.
A Model Context Protocol server that enables AI assistants to interact with Databricks workspaces, allowing them to browse Unity Catalog, query metadata, sample data, and execute SQL queries.
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