Cloudera AI MCP

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About Cloudera AI MCP

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#ai #cloudera #machine-learning

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Overview

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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Frequently asked questions

What are the runtime requirements?

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

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.

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Basic information

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