mcp_server_for_databricks

mcp_server_for_databricks

0
databases

This project offers a server interface to Databricks, facilitating data retrieval and management via FastMCP commands. It simplifies interaction with Databricks resources, enhancing efficiency through OAuth authentication and metadata provision, without the need for manual token management.

MCP Server for Databricks Interaction

1. Project Aim

This project provides a server application that acts as an interface to a Databricks workspace. It utilizes the FastMCP framework to expose tools that allow users to query and retrieve information about Databricks resources like schemas, tables, samples, and job results directly through MCP commands.

The primary goal is to simplify common Databricks metadata retrieval tasks for users interacting via the MCP interface, leveraging the Databricks SDK and CLI for backend communication.

Main goal and features of the server:

This model context protocol aims to provide tools be used with an IDE to provide your agents the context about your Databricks environment (though the tools that are provided are not only useful for IDE based development work). By doing so, it increases the effectiveness of agents while assisting you writing code for your workspace and minimizes the amount of additional information you need to provide manually. This is achieved through the following architecture:

  1. OAuth U2M authentication: This server uses Databricks cli to provide authentication between you and your Databricks workspace. This removes the need for creating tokens and storing them locally that is a requirement for most alternative solutions.
  2. Provide metadata for your catalog objects: The existing mcp tools provide metadata for your catalogs, schemas and tables using Databricks SDK. Similarly, you can get the run results for your workflows for any improvements or troubleshooting needs for yourself.
  3. Provide sample data for tables: When retrieving information related to a table with get_table_sample_tool, this server also retrieves sample rows using an sql warehouse that can be accessed by the user. The sample rows are then added to the metadata of each column to enrich the context provided.

2. Installation and Initialization

Prerequisites

  • Python (version 3.x recommended)
  • uv (Python package installer and virtual environment manager). You can install it following the instructions at https://github.com/astral-sh/uv.
  • databricks-cli installed and accessible in your system's PATH.

Important Note: The databricks_cli available from pypi has the version 0.18 which is quite old and not supported for this project. To install the latest version of databricks_cli, please go to the official Databricks page and follow the directions

Installation

  1. Clone the repository: 
    git clone <repository_url>
cd <repository_directory>
  2. Create a virtual environment and install dependencies: 
    uv venv  # Create a virtual environment (e.g., .venv)
uv sync  # Install dependencies using pyproject.toml and uv.lock
source .venv/bin/activate # Activate the virtual environment (use `.venv\\Scripts\\activate` on Windows)

Initialization

Before running the server for the first time, you need to configure its connection to your Databricks workspace:

  1. Run the initialization script:

    python init.py

  2. Follow the prompts:

    • Enter your Databricks workspace URL (e.g., https://adb-xxxxxxxxxxxx.azuredatabricks.net).
    • The script will initiate the Databricks CLI OAuth login flow using the profile name mcp_server_for_databricks. Follow the instructions provided by the CLI in your terminal/browser to authenticate.
    • Select an available SQL Warehouse from the list provided. This warehouse will be used for executing metadata queries by the available mcp tools.
    • Enter the desired sample size (number of rows) to retrieve when using the table sampling tool. Press Enter to use the default (5).

  3. Configuration Saved: The script will save the workspace URL, selected warehouse ID/name, and sample size into a config.yaml file in the project root.

  4. (For Cursor) Adding to Cursor IDE

Once the server is initialized and dependencies are installed, you can add it to Cursor:

  1. Open Cursor Settings: Navigate to Cursor Settings > Features > MCP.
  2. Add New MCP Server: Click on the + Add New MCP Server button.
  3. Configure the Server: Paste the mcp server details as shown in the example below
{
  "mcpServers": {
    "mcp_server_for_databricks": {
      "command": "/path/to/uv/executable/uv",
      "args": [
        "--directory",
        "/PATH/TO/THIS/REPO",
        "run",
        "main.py"
      ]
    }
  }
}
  1. Save and Refresh: Save the configuration. You might need to click the refresh button for the server in the MCP list to populate its tools.

3. How it Works

  • Server Framework: Uses FastMCP to define tools and handle communication via standard input/output (stdio).
  • Databricks Connection: Interacts with Databricks using the databricks-sdk for Python.
  • Authentication:
    • Relies on a databricks-cli profile named mcp_server_for_databricks.
    • The main.py script uses subprocess to call databricks auth login and databricks auth token to obtain and use authentication tokens for the SDK client. Authentication happens during the initial server startup or on the first tool call if the server hasn't initialized yet.
  • Information Retrieval:
    • Metadata Queries: Most schema and table listing operations are performed by executing SQL statements (SHOW DATABASES, SHOW TABLES) via the client.statement_execution.execute_statement SDK method against the configured SQL Warehouse.
    • Detailed Table Info: Fetches comprehensive table details (column types, comments, properties, etc.) using the client.tables.get SDK method.
    • Table Sampling: Retrieves sample data by executing a SELECT * ... LIMIT N query via the client.statement_execution.execute_statement method.
    • Job Results: Uses the client.jobs API (list, list_runs, get_run, get_run_output) to find jobs and retrieve output from specific runs.
  • Configuration: Reads connection details (workspace URL, warehouse ID, sample size) from config.yaml upon startup.

4. MCP Tools Provided

The following tools are exposed by this server:

  • get_schemas(catalog: str)

    • Description: Retrieves a list of all schemas and the tables contained within each schema for a specified catalog.
    • Arguments:
      • catalog (str): The name of the Databricks catalog to query.
    • Returns: A list of objects, each containing catalog, schema_name, and a list of tables.

  • get_table_sample_tool(catalog: str, schema_name: str, table: str)

    • Description: Returns detailed metadata for a specific table, including column information (type, comment, nullability) and a sample number of rows from the table data. The number of sample rows is determined by the sample_size configured during initialization. Optionally saves the metadata and sample data to the .input_data/ directory if configured in config.yaml.
    • Arguments:
      • catalog (str): The catalog name.
      • schema_name (str): The schema name.
      • table (str): The table name.
    • Returns: The structure of the dictionary returned is as follows: 
      {
    "name": "string (table name)",
    "catalog_name": "string (catalog name)",
    "schema_name": "string (schema name)",
    "table_type": "string (e.g., MANAGED, EXTERNAL, VIEW)",
    "data_source_format": "string (e.g., DELTA, CSV, PARQUET)",
    "columns": [
        {
            "name": "string (column name)",
            "type_name": "string (column data type e.g. INT, STRING)",
            "comment": "string (column comment or null)",
            "nullable": "boolean",
            "partition_index": "integer (or null, 0-indexed if column is a partition column)",
            "mask": "object (column mask details or null)",
            "sample_values": ["list of sample values (mixed types)"]
        }
        // ... more columns
    ],
    "comment": "string (table comment or null)",
    "properties": {
        "property_key": "property_value"
        // ... more properties
    },
    "storage_location": "string (path to storage or null for managed tables/views)",
    "view_definition": "string (SQL definition if it's a view, else null)",
    "table_id": "string (unique table identifier)",
    "created_at": "string (ISO 8601 timestamp or null)",
    "updated_at": "string (ISO 8601 timestamp or null)",
    "deleted_at": "string (ISO 8601 timestamp or null, if applicable)",
    "row_filter": "object (row filter details or null)",
    "owner": "string (owner of the table, e.g., user or group)"
}

  • get_schema_metadata(catalog_name: str, schema_name: str)

    • Description: Retrieves metadata for a specific schema, including the schema comment and details for each table within it (table comment, creation timestamp, table type, owner).
    • Arguments:
      • catalog_name (str): The catalog name.
      • schema_name (str): The schema name.
    • Returns: The structure of the dictionary returned is as follows (the tables dictionary will contain an entry for each table in the schema): 
      {
    "schema_comment": "string (Schema comment or null)",
    "tables": {
        "your_table_name_here": {
            "comment": "string (Table comment or null)",
            "created_at": "string (ISO 8601 timestamp)",
            "table_type": "string (e.g., MANAGED, EXTERNAL, VIEW)",
            "owner": "string (Table owner)"
        }
        // ... more tables
    }
}

  • get_job_run_result(job_name: str, filter_for_failed_runs: bool = False)

    • Description: Retrieves the results from the most recent run of a specified Databricks job. Provides options to get the latest run regardless of status or specifically the last failed run.
    • Arguments:
      • job_name (str): The exact name of the Databricks job.
      • filter_for_failed_runs (bool, optional): If True, retrieves the result of the last failed run only. Defaults to False (retrieves the last completed run).
    • Returns: A string containing the error message, error traceback, and metadata associated with the selected job run.

Known Issues:

  • Currently the JWT token is not automatically refreshed after its' validity threshold passes. Because of this, if the MCP server has been running for more than 12 hours it needs to be restarted. This will be fixed soon
  • get_table_sample_tool can retrieve too much context for very wide tables or tables that have a lot of comments. If the user encounters this issue, sample_size in config file can be reduced to minimize this risk.
  • Current version requires the user to change workspace manually when the user wants to retrieve information from another Databricks workspace which is a significant problem for organizations that haven't transitioned to Unity Catalog yet. The ability to specify different workspaces will be made available with later versions