CML MCP Server

An MCP server for interacting with Cloudera Machine Learning (CML).

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

- Python 3.8+
- Required Python packages:

- mcp[cli]>=1.2.0
- requests>=2.31.0
- 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):

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

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

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

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

To use this server with Claude for Desktop:
- Create aclaude_desktop_config.jsonfile 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 theuvPython 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" } } } }

Theuvmethod provides better dependency isolation and faster startup times compared to standard Python execution.

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

- 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

- 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)

- 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

- 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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