# 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
1. Install the required packages:
```bash
pip install mcp[cli] requests
```
Or with uv:
```bash
uv pip install mcp[cli] requests
```
2. Set up environment variables (optional):
```bash
# 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"
```
3. Download the SSL certificate from your CML server (if using a self-signed certificate):
```bash
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:
```bash
# 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:
```bash
python3 cml_mcp_server.py --help
```
You can also specify custom parameters:
```bash
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:
```bash
python direct_list_projects.py
```
## Integration with Claude for Desktop
To use this server with Claude for Desktop:
1. Create a `claude_desktop_config.json` file in your Claude for Desktop configuration directory
2. Add the following configuration (update the path to match your server location):
```json
{
"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):
```json
{
"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
