// MCP TOOL REFERENCE

MLFLOW MCP SERVER TOOLS

40 tools from the MLflow MCP Server MCP Server, categorised by risk level.

// ALL 40 MLFLOW MCP SERVER TOOLS

READ 27 tools

Read compare_runs Compare runs side-by-side with full metrics and params. Runs can be large — keep the list short. Read get_artifact_content Read and return artifact content (for text/json files) Read get_best_run Get the best run by a specific metric (e.g., highest accuracy, lowest loss). Works with metrics containing ... Read get_experiment_by_name Get experiment details by name (more convenient than ID) Read get_experiment_metrics Get all unique metric names used across all runs in an experiment Read get_experiment_params Get all unique parameter names used across all runs in an experiment Read get_experiment_tags Get all unique tag keys used across all runs in an experiment Read get_experiments Get all experiments Read get_latest_versions Get latest model versions for each stage (e.g. 'Staging', 'Production'). Read get_logged_model Get detailed information about a specific logged model by its ID. Read get_model_version Get specific model version details (metrics, stage, run_id) Read get_model_version_by_alias Get a model version by its alias (e.g. 'champion', 'production'). Read get_model_versions Get all versions of a registered model Read get_parent_run Get the parent run of a nested run. Returns None if the run has no parent. Read get_registered_model Get full details of a registered model including all versions and aliases. Can be large for models with man... Read get_registered_models List all registered models in the model registry Read get_run Get detailed information about a specific run. Run data can be large — avoid fetching many runs at once. Read get_run_artifact Download and return the local path to a specific artifact Read get_run_artifacts List artifacts for a specific run. Use 'path' to browse into directories (e.g., 'configs') Read get_run_metric Get the full history of a specific metric for a run Read get_run_metrics Get all metrics for a specific run with their latest values Read get_runs get_runs Read health Check MLflow server health and connectivity Read query_runs query_runs Read search_experiments search_experiments Read search_logged_models search_logged_models Read search_runs_by_tags Find runs with specific tags (e.g., {'team': 'nlp', 'production': 'true'}). Runs can be large — use wise li...

WRITE 8 tools

Write transition_model_version_stage transition_model_version_stage Write copy_model_version copy_model_version Write register_model register_model Write set_experiment_tag Set a tag on an experiment. Write set_model_alias set_model_alias Write set_registered_model_tag Set a tag on a registered model (e.g. problem_type, team, framework). Write set_run_tag Set a tag on a run (e.g. annotate best model, flag for review). Write update_model_version Update the description of a model version.

DESTRUCTIVE 5 tools

Destructive delete_experiment Delete an experiment and all its runs. Moves to the 'deleted' lifecycle stage — not shown in UI or queries,... Destructive delete_model_alias Remove an alias from a registered model (e.g. revoke 'champion'). The alias is permanently removed; the mod... Destructive delete_model_version Delete a specific model version from the registry. Irreversible — the version and its metadata cannot be re... Destructive delete_registered_model Delete an entire registered model and all its versions. Irreversible — all versions, aliases, and tags are ... Destructive delete_run Delete a run. Moves it to the 'deleted' lifecycle stage — not shown in UI or queries, but recoverable via t...

// FAQ

How many tools does the MLflow MCP Server MCP server have? +

The MLflow MCP Server MCP server exposes 40 tools across 3 categories: Read, Write, Destructive.

How do I enforce policies on MLflow MCP Server tools? +

Route the MLflow MCP Server server through the PolicyLayer gateway. Define allow, deny, or approval rules per tool in the dashboard; they are enforced on every call before it reaches the server.

What risk categories do MLflow MCP Server tools fall into? +

MLflow MCP Server tools are categorised as Read (27), Write (8), Destructive (5). Each category has a recommended default policy.

Enforce policy on every MLflow MCP Server tool call.

Start from MLflow MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.

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