MCP_cloudera

# Cloudera ML Model Control Protocol (MCP) This MCP implements a Python-based integration with Cloudera Machine Learning, allowing Claude to interact with CML services programmatically. ## Features 1. **Upload Folders**: Upload entire folders to your CML project while preserving directory structure 2. **Create Jobs**: Create new CML jobs with customizable settings 3. **List Jobs**: View all jobs in your project with their current status 4. **Delete Jobs**: Remove individual jobs or all jobs in a project 5. **Get Project ID**: Retrieve project ID from a project name 6. **List Project Files**: View files and directories in your project 7. **Model Management**: Create, list, and manage ML models and deployments 8. **Experiment Tracking**: Log and manage ML experiments and runs 9. **Application Management**: Create, update, and manage CML applications ## Installation 1. Clone this repository 2. Install dependencies: ```bash pip install -r requirements.txt ``` ## Configuration The MCP requires the following configuration: 1. **host**: Your CML instance URL (e.g., "https://ml-xxxx.cloudera.site") 2. **api_key**: Your API key for authentication 3. **project_id**: Your CML project ID (optional - you can now get it by project name) You can provide this configuration in code when initializing the MCP, or use environment variables: ```bash export CLOUDERA_ML_HOST="https://ml-xxxx.cloudera.site" export CLOUDERA_ML_API_KEY="your-api-key" # Optional: export CLOUDERA_ML_PROJECT_ID="your-project-id" ``` ### URL Configuration Notes - The host URL should not include duplicate "https://" prefixes - Trailing slashes are automatically handled - The MCP will automatically format URLs correctly ## Running the MCP Server This MCP can be run as a server that allows Claude to interact with Cloudera ML. ### Set up the environment Copy the example .env file and add your credentials: ```bash cp .env.example .env # Edit .env with your credentials ``` ### Start the server ```bash ./server.py ``` The server uses the stdio transport by default, which allows it to connect directly to Claude. ### Usage with Claude Desktop To use this server with the Claude Desktop app, add the following configuration to the "mcpServers" section of your `claude_desktop_config.json`: ```json { "mcpServers": { "cloudera-ml-mcp-server": { "command": "python", "args": [ "/path/to/MCP_cloudera/server.py" ], "env": { "CLOUDERA_ML_HOST": "https://ml-xxxx.cloudera.site", "CLOUDERA_ML_API_KEY": "your-api-key" } } } } ``` Replace `/path/to` with your path to this repository and set the environment variables. ## Using in Your Own Python Code You can also import and use the MCP in your own Python code: ```python from MCP_cloudera.src import ClouderaMCP # Initialize the MCP config = { "host": "https://ml-xxxx.cloudera.site", "api_key": "your-api-key" } cloudera = ClouderaMCP(config) # Get project ID by name project_info = cloudera.get_project_id(project_name="my-project-name") project_id = project_info["project_id"] print(f"Project ID: {project_id}") # List project files files = cloudera.list_project_files(project_id=project_id) print(files) # Upload a folder result = cloudera.upload_folder( folder_path="/path/to/local/folder", ignore_folders=["node_modules", ".git"] ) ``` ## Command-line Testing You can test the MCP functions from the command line using the provided script: ```bash ./run_mcp.py [--host HOST] [--api-key API_KEY] [--project-id PROJECT_ID] COMMAND [command options] ``` Where `COMMAND` is one of: - `list_jobs` - List all jobs in the project - `upload_folder` - Upload a folder to the project - `create_job` - Create a new job - `delete_job` - Delete a specific job - `delete_all_jobs` - Delete all jobs in the project - `get_project_id` - Get project ID from a project name (`--project-name` required) - `list_project_files` - List files in a project - `list_models` - List ML models in a project - `list_model_deployments` - List model deployments - `list_experiments` - List experiments in a project - `list_job_runs` - List job runs ### Example: Listing Project Files ```bash ./run_mcp.py --host "https://ml-xxxx.cloudera.site" --api-key "your-api-key" list_project_files --project-id "your-project-id" ``` ## Requirements - Python 3.8+ - requests - pathlib - python-dotenv - mcp[cli] ## License MIT