Which integrations are available for this server?

Enables interaction with Cloudera Machine Learning (CML) to manage projects, perform file operations like uploading and reading, and handle job lifecycle management including creation, execution, and scheduling.

How do I use Cloudera Machine Learning (CML) MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Cloudera Machine Learning (CML) MCP Server List all my projects and show the status of the most recent job runs" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

CML MCP Server

A standalone MCP (Model Context Protocol) server for interacting with Cloudera Machine Learning (CML).

Requirements

Python 3.8+
Required Python packages:
- mcp[cli]>=1.2.0
- requests>=2.31.0

Installation

Install the required packages:

pip install mcp[cli] requests

Or with uv:

uv pip install mcp[cli] requests

Set up environment variables (optional):

# Traditional environment variables export CML_API_TOKEN="your_api_token_here" export CML_BASE_URL="https://your-cml-instance.cloudera.com" # MCP configuration environment variables (preferred) export CLOUDERA_ML_API_KEY="your_api_token_here" export CLOUDERA_ML_HOST="https://your-cml-instance.cloudera.com" # Certificate path (optional) export CML_CERT_FILE="/path/to/your/certificate.pem"

Download the SSL certificate from your CML server (if using a self-signed certificate):

python download_certificate.py

This will download the certificate from the CML server specified in the CLOUDERA_ML_HOST or CML_BASE_URL environment variable and save it to cml_ca.pem.

Usage

You can run the server using any of these commands:

# Using standard Python python3 cml_mcp_server.py # Using uv uv run cml_mcp_server.py # Using uvx uvx cml_mcp_server.py

For help and configuration information:

python3 cml_mcp_server.py --help

You can also specify custom parameters:

python3 cml_mcp_server.py --token "your_api_token" --url "https://your-cml-instance.cloudera.com" --cert "/path/to/your/certificate.pem"

Direct Usage

You can also use the direct script to list projects without using the MCP server:

python direct_list_projects.py

Integration with Claude for Desktop

To use this server with Claude for Desktop:

Create a claude_desktop_config.json file in your Claude for Desktop configuration directory
Add the following configuration (update the path to match your server location):

{ "mcpServers": { "cml": { "command": "uv", "args": ["run", "/full/path/to/cml_mcp_server.py"], "env": { "CLOUDERA_ML_HOST": "https://your-cml-instance.cloudera.com", "CLOUDERA_ML_API_KEY": "your-api-key-here" } } } }

Alternatively, you can use the uv Python package manager to run the server (recommended):

{ "mcpServers": { "cml": { "command": "python3", "args": ["/full/path/to/cml_mcp_server.py"], "env": { "CLOUDERA_ML_HOST": "https://your-cml-instance.cloudera.com", "CLOUDERA_ML_API_KEY": "your-api-key-here" } } } }

The uv method provides better dependency isolation and faster startup times compared to standard Python execution.

Available Tools

The server provides the following MCP tools for interacting with CML:

Project Management

list_projects: List all CML projects the user has access to
create_project: Create a new CML project
get_project: Get details of a specific CML project

File Operations

list_files: List files in a CML project at the specified path
read_file: Read the contents of a file from a CML project
upload_file: Upload a file to a CML project
rename_file: Rename a file in a CML project
patch_file: Update file metadata (rename, move, or change attributes)

Job Management

list_jobs: List all jobs in a CML project
create_job: Create a new job in a CML project
create_job_from_file: Create a job from an existing file in a CML project
run_job: Run a job in a CML project
list_job_runs: List all runs for a job in a CML project
stop_job_run: Stop a running job in a CML project
schedule_job: Schedule a job to run periodically using a cron expression

Runtime Management

list_runtime_addons: List all available runtime addons (e.g., Spark3, GPU)
download_ssl_cert: Download the SSL certificate from the CML server

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quality - not tested

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Resources

GitHub Repository

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Cloudera Machine Learning (CML) MCP Server