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Glama
arahimi-hims

Lakeflow MCP Server

by arahimi-hims
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
DATABRICKS_HOSTYesThe URL of the Databricks host.
DATABRICKS_TOKENYesYour Databricks personal access token.

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
build_wheelA

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_wheelB

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_jobB

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_runB

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_runsC

Lists runs for a specific job.

Args:
    job_id: The ID of the job to list runs for.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.6/5.0

Scored across 5 tools

Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap: building a wheel, creating a job, listing job runs, triggering a run, and uploading a wheel. The descriptions specify unique actions on different resources (wheel files vs. Databricks jobs), making misselection unlikely.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (e.g., build_wheel, create_job, list_job_runs, trigger_run, upload_wheel). The verbs are clear and descriptive, and there are no deviations in naming style across the set.

Tool Count5/5

With 5 tools, this server is well-scoped for its purpose of managing Databricks job workflows with Python wheels. Each tool earns its place by covering distinct steps in the process, from building and uploading wheels to job creation and execution.

Completeness4/5

The tool set provides strong coverage for core workflows: building, uploading, job creation, triggering runs, and monitoring runs. Minor gaps exist, such as no tools for updating or deleting jobs, but agents can likely work around this for basic operations.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources