Check the MLflow server's health and connectivity to confirm it is operational and reachable before running other commands.
MIT
523,718 tools. Updated 2026-09-06 13:58
"MLflow" matching MCP tools:
- Retrieve attachments linked to a trace by supplying its ID; used to inspect trace metadata in Databricks MLflow.MIT
- Retrieve a trace attachment using trace ID and attachment ID. Compatible with Databricks MLflow (OSS servers return 404).MIT
- Delete an MLflow experiment by providing its experiment ID. Removes the experiment and associated runs from the Databricks workspace.MITDestructive
- Retrieve an MLflow experiment's details using its experiment ID for analysis or monitoring.MIT
- Delete a specific version of a registered MLflow model by providing the model name and version number.MITDestructive
Matching MCP Servers
- AlicenseAqualityAmaintenanceA Model Context Protocol server that enables LLMs to interact with MLflow tracking servers, allowing users to query experiments, analyze runs, compare metrics, manage the model registry, and promote models through natural language.4015MIT
- AlicenseBqualityAmaintenanceMLflow MCP — 82 tools for experiments, runs, registered models, traces, assessments. MLflow 3 GenAI traces support with analyze-failed-traces workflow Prompt.82921MIT
- Retrieve a specific model version from a registered MLflow model by providing the model name and version number.MIT
- Create a new MLflow experiment to track machine learning runs with unique name, optional artifact location, and tags.MIT
- Create a logged model entity in an MLflow experiment by specifying experiment ID, model name, type, source run, parameters, and tags.MIT
- Search and list MLflow experiments with optional filters, pagination, and sorting to manage experiment tracking in Databricks.MIT
- Create a new version of a registered MLflow model by specifying the model name and source URI.MIT
- Update an MLflow experiment's name, tags, or lifecycle stage using the Databricks REST API.MIT
- Create a new MLflow experiment with a required name and optional artifact location and tags.MIT
- List and filter MLflow model versions using search criteria such as filter, ordering, and pagination.MIT
- List all Managed MLflow Tracking Servers in SageMaker to review and manage your MLflow environments.Apache 2.0
- List all MLflow experiments to view their status and last update time for monitoring MLOps workflows.Apache 2.0
- Execute bash commands within the sktime container to install Python packages or inspect files.BSD 3-Clause
- List all registered ML models with their versions and stages from the MLflow registry to track model lineage and deployment readiness.Apache 2.0
- Save an estimator or pipeline handle to a specified path using sktime MLflow integration for model persistence and management.BSD 3-Clause
- Compare metrics across multiple MLflow runs to evaluate model performance. Use side-by-side analysis to identify the most effective run for your needs.Apache 2.0
- Retrieve MLflow runs for an experiment to view metrics, parameters, and status. Filter by criteria such as 'metrics.auc > 0.9' to isolate specific runs.Apache 2.0