Actions for AI agents

Explore exact operations, provider setup, pricing, and safety details before using them with Aident Loadout.

Showing 10609-10632 of 33122 Actions, ordered by recent successful use.

Databricks Tools
Databricks Mktplace Provider Exchanges Delete Listing
Tool to remove the association between a marketplace exchange and a listing. Use when you need to disassociate an exchange from a provider listing. This removes the listing from the private exchange, and it will no longer be shared with the curated set of customers in that excha…
FreeWrite action
Databricks Tools
Databricks Ml Experiments Create Experiment
Tool to create a new MLflow experiment for tracking machine learning runs and models. Use when you need to organize and track ML experiments within Databricks. Returns RESOURCE_ALREADY_EXISTS error if an experiment with the same name already exists.
FreeWrite action
Databricks Tools
Databricks Ml Experiments Create Logged Model
Tool to create a new logged model in MLflow that ties together model metadata, parameters, metrics, and artifacts. Use when you need to create a LoggedModel object as part of the unified 'log + register' workflow introduced in MLflow 2.8. LoggedModel objects persist throughout a…
FreeWrite action
Databricks Tools
Databricks Ml Experiments Create Run
Tool to create a new MLflow run within an experiment for tracking machine learning execution. Use when starting a new ML training run, experiment execution, or data pipeline that needs parameter and metric tracking. Returns the created run with a unique run_id for subsequent met…
FreeWrite action
Databricks Tools
Databricks Ml Experiments Delete Experiment
Tool to delete an MLflow experiment and associated metadata, runs, metrics, params, and tags. Use when you need to remove an experiment from Databricks. If the experiment uses FileStore, artifacts associated with the experiment are also deleted.
FreeWrite action
Databricks Tools
Databricks Ml Experiments Delete Logged Model
Tool to delete a logged model from MLflow tracking. Use when you need to permanently remove a LoggedModel from the tracking server. The deletion is permanent and cannot be undone. LoggedModels track a model's lifecycle across different training and evaluation runs.
FreeWrite action
Databricks Tools
Databricks Ml Experiments Delete Logged Model Tag
Tool to delete a tag from a logged model in MLflow. Use when you need to remove metadata from a LoggedModel object. This operation is irreversible and permanently removes the tag from the logged model. Part of MLflow 3's logged model management capabilities.
FreeWrite action
Databricks Tools
Databricks Ml Experiments Delete Run
Tool to mark an MLflow run for deletion in ML experiments. Use when you need to remove a specific run from Databricks. This is a soft delete operation - the run is marked for deletion rather than immediately removed and can be restored unless permanently deleted.
FreeWrite action
Databricks Tools
Databricks Ml Experiments Delete Runs
Tool to bulk delete runs in an ML experiment created before a specified timestamp. Use when you need to clean up old experiment runs. Only runs created prior to or at the specified timestamp are deleted. The maximum number of runs that can be deleted in one operation is 10000.
FreeWrite action
Databricks Tools
Databricks Ml Experiments Delete Tag
Tool to delete a tag from an MLflow experiment run. Use when you need to remove run metadata. This operation is irreversible and permanently removes the tag from the run.
FreeWrite action
Databricks Tools
Databricks Ml Experiments Finalize Logged Model
Tool to finalize a logged model in MLflow by updating its status to READY or FAILED. Use when custom model preparation logic is complete and you need to mark the model as ready for use or indicate that upload failed. This is part of the experimental logged models feature introdu…
FreeWrite action
Databricks Tools
Databricks Ml Experiments Get By Name
Tool to retrieve MLflow experiment metadata by name. Use when you need to get experiment details using the experiment name. Returns deleted experiments but prefers active ones if both exist with the same name. Throws RESOURCE_DOES_NOT_EXIST if no matching experiment exists.
FreeWrite action
Databricks Tools
Databricks Ml Experiments Get Experiment
Tool to retrieve metadata for an MLflow experiment by ID. Use when you need to get experiment details including name, artifact location, lifecycle stage, and tags. Works on both active and deleted experiments.
FreeWrite action
Databricks Tools
Databricks Ml Experiments Get Logged Model
Tool to fetch logged model metadata by unique ID. Use when you need to retrieve a LoggedModel object representing a model logged to an MLflow Experiment. Returns comprehensive model information including metrics, parameters, tags, and artifact details.
FreeWrite action
Databricks Tools
Databricks Ml Experiments Get Permission Levels
Tool to retrieve available permission levels for a Databricks ML experiment. Use when you need to understand what permission levels can be assigned to users, groups, or service principals for a specific experiment. Returns permission levels (CAN_READ, CAN_EDIT, CAN_MANAGE) with…
FreeWrite action
Databricks Tools
Databricks Ml Experiments Get Permissions
Tool to retrieve permissions for an MLflow experiment. Use when you need to check who has access to an experiment and their permission levels. Note that notebook experiments inherit permissions from their corresponding notebook, while workspace experiments have independent permi…
FreeWrite action
Databricks Tools
Databricks Ml Experiments Get Run
Tool to retrieve complete information about a specific MLflow run including metadata, metrics, parameters, tags, inputs, and outputs. Use when you need to get details of a run by its run_id. Returns the most recent metric values when multiple metrics with the same key exist.
FreeWrite action
Databricks Tools
Databricks Ml Experiments Log Batch
Tool to log a batch of metrics, parameters, and tags for an MLflow run in a single request. Use when you need to efficiently log multiple metrics, params, or tags simultaneously. Items within each type are processed sequentially in the order specified. The combined total of all…
FreeWrite action
Databricks Tools
Databricks Ml Experiments Log Inputs
Tool to log dataset inputs to an MLflow run for tracking data sources used during model development. Use when you need to track metadata about datasets used in ML experiment runs, including information about the dataset source, schema, and tags. Enables logging of dataset inputs…
FreeWrite action
Databricks Tools
Databricks Ml Experiments Log Logged Model Params
Tool to log parameters for a logged model in MLflow. Use when you need to attach hyperparameters or metadata to a LoggedModel object. A param can be logged only once for a logged model, and attempting to overwrite an existing param will result in an error. Available in MLflow 2.…
FreeWrite action
Databricks Tools
Databricks Ml Experiments Log Metric
Tool to log a metric for an MLflow run with timestamp. Use when you need to record ML model performance metrics like accuracy, loss, or custom evaluation metrics. Metrics can be logged multiple times with different timestamps and values are never overwritten - each log appends t…
FreeWrite action
Databricks Tools
Databricks Ml Experiments Log Model
Tool to log a model artifact for an MLflow run (Experimental API). Use when you need to record model metadata including artifact paths, flavors, and versioning information for a training run. The model_json parameter should contain a complete MLmodel specification in JSON string…
FreeWrite action
Databricks Tools
Databricks Ml Experiments Log Outputs
Tool to log dataset outputs from an MLflow run for tracking data generated during model development. Use when you need to track metadata about datasets produced by ML experiment runs, including information about predictions, model outputs, or generated data. Enables logging of d…
FreeWrite action
Databricks Tools
Databricks Ml Experiments Log Param
Tool to log a parameter for an MLflow run as a key-value pair. Use when you need to record hyperparameters or constant values for ML model training or ETL pipelines. Parameters can only be logged once per run and cannot be changed after logging. Logging identical parameters is i…
FreeWrite action

Databricks Mktplace Provider Exchanges Delete Listing

Databricks Ml Experiments Create Experiment

Databricks Ml Experiments Create Logged Model

Databricks Ml Experiments Create Run

Databricks Ml Experiments Delete Experiment

Databricks Ml Experiments Delete Logged Model

Databricks Ml Experiments Delete Logged Model Tag

Databricks Ml Experiments Delete Run

Databricks Ml Experiments Delete Runs

Databricks Ml Experiments Delete Tag

Databricks Ml Experiments Finalize Logged Model

Databricks Ml Experiments Get By Name

Databricks Ml Experiments Get Experiment

Databricks Ml Experiments Get Logged Model

Databricks Ml Experiments Get Permission Levels

Databricks Ml Experiments Get Permissions

Databricks Ml Experiments Get Run

Databricks Ml Experiments Log Batch

Databricks Ml Experiments Log Inputs

Databricks Ml Experiments Log Logged Model Params

Databricks Ml Experiments Log Metric

Databricks Ml Experiments Log Model

Databricks Ml Experiments Log Outputs

Databricks Ml Experiments Log Param