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Use KappaML tools for model management, prediction, learning, and metrics monitoring.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "KappaML MCP Server" yet — see the docs or source repo.
Use the KappaML tools to list available models, the latest training job status, and each model's last updated time, then summarize them in a table.
A structured table summarizing models, training status, and last updated times.
Use KappaML's prediction interface to run inference on this input dataset and return the prediction results, confidence scores, and key feature explanations.
A list of prediction results with confidence scores and brief explanations.
Use the KappaML tools to fetch key metrics from the last five training runs of a specified model and list available checkpoints to help decide whether a rollback is needed.
Training metric trends, available checkpoints, and a brief basis for whether rollback is recommended.
Expose Azure ML endpoints as MCP tools for natural-language AI agent calls.
Production-ready MCP server for query normalization, retrieval, and RAG prompt building.
Centralize knowledge, run semantic search, ingest documents, and generate RAG answers.
Expose GraphQL operations as AI-callable tools for integration and automation.
Expose OpenAPI endpoints as MCP tools for LLM-driven REST API access.
Let AI manage Kubernetes clusters and Helm for deployment and troubleshooting.