Schema | Lakeflow MCP Server

Describes the environment variables required to run the server.

Name	Required	Description	Default
`DATABRICKS_HOST`	Yes	The URL of the Databricks host.
`DATABRICKS_TOKEN`	Yes	Your Databricks personal access token.

Features and capabilities supported by this server

Capability	Details
`tools`	{ "listChanged": false }
`prompts`	{ "listChanged": false }
`resources`	{ "subscribe": false, "listChanged": false }
`experimental`	{}

Functions exposed to the LLM to take actions

Name	Description
build_wheel	Builds the Python wheel using 'uv build --wheel'. `Args: target: The path to the directory containing pyproject.toml. Returns: The path to the generated wheel file.`
upload_wheel	Uploads a local wheel file to the Databricks workspace. `Args: local_path: The local path to the wheel file. Returns: The full remote path of the uploaded wheel.`
create_job	Creates a Databricks job with the specified wheel and entry point. `Args: job_name: The name of the job to create. package_name: The name of the Python package. remote_wheel_path: The remote path to the uploaded wheel file. Returns: The ID of the created job.`
trigger_run	Triggers a run of the specified job. `Args: job_id: The ID of the job to run. job_args: A list of Python parameters to pass to the run. Returns: The ID of the triggered run.`
list_job_runs	Lists runs for a specific job. `Args: job_id: The ID of the job to list runs for.`

Interactive templates invoked by user choice

Name	Description
No prompts

Contextual data attached and managed by the client

Name	Description
No resources

Lakeflow MCP Server