Cloudera AI MCP

What is Cloudera AI MCP?

Cloudera AI MCP is a Python-based Model Context Protocol server that integrates with Cloudera Machine Learning (CML), allowing Claude to interact with CML services programmatically.

How to use Cloudera AI MCP?

Clone the repository, install dependencies with pip install -r requirements.txt, configure your CML host and API key via environment variables or code, then run ./server.py or import ClouderaMCP in your Python code. The server uses stdio transport and connects to Claude Desktop via claude_desktop_config.json.

Key features of Cloudera AI MCP

• Upload folders while preserving directory structure
• Create, list, and delete CML jobs
• Retrieve project ID from a project name
• List project files and directories
• Manage ML models, deployments, and experiments
• Create and manage CML applications

Use cases of Cloudera AI MCP

• Automate folder uploads and job creation from Claude
• Query and manage ML experiments and model deployments
• List and clean up jobs across a CML project
• Integrate CML project management into conversational workflows

FAQ from Cloudera AI MCP

What does Cloudera AI MCP do vs alternatives?

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What are the runtime requirements?

Python 3.8+ and the requests, pathlib, python-dotenv, and mcp[cli] packages.

Where does data live?

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What transports and authentication does it use?

The server uses stdio transport by default. Authentication requires a CML API key, provided via environment variables or configuration.

Are there known limits?

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