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

git clone <repository-url>
cd databricks-mcp-server
pip install -e .

Install Development Dependencies

pip install -e ".[dev]"

Configuration

Environment Variables

Set the following environment variables:

export DATABRICKS_HOST="https://your-workspace.cloud.databricks.com"
export DATABRICKS_TOKEN="your-personal-access-token"
export DATABRICKS_WAREHOUSE_ID="your-warehouse-id"  # Optional but recommended
export LOG_LEVEL="INFO"  # Optional

Configuration File

Alternatively, create a config.json file:

{
  "databricks_host": "https://your-workspace.cloud.databricks.com",
  "databricks_token": "your-personal-access-token", 
  "databricks_warehouse_id": "your-warehouse-id",
  "log_level": "INFO"
}

Usage

Running the Server

# Run directly
python -m databricks_mcp_server.server

# Or use the installed command
databricks-mcp-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:

{
  "mcpServers": {
    "databricks": {
      "command": "databricks-mcp-server",
      "env": {
        "DATABRICKS_HOST": "https://your-workspace.cloud.databricks.com",
        "DATABRICKS_TOKEN": "your-token"
      }
    }
  }
}

Available Tools

Catalog Operations

list_catalogs: List all Unity Catalog catalogs
list_schemas: List schemas in a catalog
list_tables: List tables in a schema

Table Operations

describe_table: Get detailed table information including columns and metadata
sample_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 warehouses
get_table_lineage: Get lineage information for tables

Resources

The server exposes Databricks resources through URIs:

databricks://catalog/{catalog_name}: Catalog information
databricks://catalog/{catalog_name}/{schema_name}: Schema information
databricks://catalog/{catalog_name}/{schema_name}/{table_name}: Table information

Examples

Basic Usage

from databricks_mcp_server import DatabricksClient

# Initialize client
client = await DatabricksClient.create()

# List catalogs
catalogs = await client.list_catalogs()
print(f"Found {len(catalogs)} catalogs")

# Get table info
table_info = await client.describe_table("main", "default", "my_table")
print(f"Table has {len(table_info.columns)} columns")

# Sample data
sample = await client.sample_table("main", "default", "my_table", limit=5)
print(f"Sampled {sample.row_count} rows")

Advanced Data Discovery

from databricks_mcp_server import UnityCatalogManager

# Initialize manager
manager = UnityCatalogManager(client)

# Discover tables with patterns
results = await manager.discover_data(
    search_patterns=["customer", "user"],
    catalogs=["main", "analytics"],
    include_metadata=True
)

print(f"Found {results.total_tables} matching tables")

Development

Running Tests

pytest

Code Formatting

black src/ tests/
isort src/ tests/

Type Checking

mypy src/

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:

export LOG_LEVEL=DEBUG
databricks-mcp-server

Configuration Validation

Use the built-in validation:

from databricks_mcp_server.utils import validate_databricks_config

validation = validate_databricks_config()
if not validation["valid"]:
    print("Configuration errors:", validation["errors"])

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

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

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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