Enables AI assistants to interact with MLflow experiments, runs, and registered models. Supports browsing experiments, retrieving run details with metrics and parameters, and querying the model registry through natural language.
Enables AI assistants to perform MLOps workflows such as experiment tracking, model registry, dataset management, pipeline orchestration, and data lineage by wrapping DVC, MLflow, and Git.
Enables access to prompt templates managed in MLflow through Claude Desktop, allowing users to instruct Claude with saved templates for repetitive tasks or common workflows.
Enables AI assistants like Claude to interact with Databricks workspaces through a secure, authenticated interface. Supports custom prompts and tools that leverage the Databricks SDK for workspace management, job execution, and SQL operations.
Provides AI agents with a toolset to query model inventories, trace dependencies, and analyze the impact of changes across machine learning models and data pipelines.
Enables Claude to inspect, configure, run, and modify individual RAG/agent pipeline stages (retrieve, rerank, generate, eval) as structured typed tool calls, and execute the full pipeline end to end.
