get_project
Get Project
Retrieve detailed information about a specific DataRobot project including its configuration, target, stage, partition settings, and advanced options.
Ask your agent in plain language. Each of these maps to a real tool on the server.
get_project
Retrieve detailed information about a specific DataRobot project including its configuration, target, stage, partition settings, and advanced options.
list_datasets
List datasets in the DataRobot AI Catalog. Returns metadata including name, size, row/column counts, and processing state.
get_deployment_monitoring
Retrieve monitoring data for a deployed model including service health statistics, accuracy metrics, data drift, and target drift. Choose which monitoring aspects to include.
make_predictions
Make real-time predictions using a model in a serverless prediction environment. Pass an array of data rows as key-value objects. Optionally include prediction explanations to understand why the model makes each prediction.
start_autopilot
Set the target variable and start Autopilot on an existing project. Autopilot automatically selects and trains the best predictive models for the specified target feature. Supports configuration of mode, metric, and advanced options.
get_deployment
Retrieve detailed information about a specific model deployment including its health status, model information, capabilities, and configuration.
list_deployments
List all model deployments with their health status, importance, and prediction usage. Useful for monitoring deployed models across the organization.
create_deployment
Deploy a trained model to production. Supports deploying from a learning model (by modelId) or from a registered model package (by modelPackageId). The deployment will be accessible for real-time or batch predictions.
manage_dataset
Manage datasets in the AI Catalog. Create a new dataset from a URL, update an existing dataset's name, or delete a dataset.
list_models
List trained models in a DataRobot project. Returns model types, training details, and performance metrics from the leaderboard.
manage_deployment
Manage an existing deployment. Update its label/description/importance, replace the champion model, or delete the deployment entirely.
list_model_packages
List model packages in the Model Registry. Model packages are deployment-ready bundles that can be deployed, shared, or used to generate compliance documentation.
create_project
Create a new DataRobot project from a dataset URL or an existing AI Catalog dataset. Optionally sets the target variable and starts Autopilot in one step.
register_model
Register a trained model from a project leaderboard as a model package in the Model Registry. Registered model packages can then be deployed, shared, and used for compliance documentation.
list_projects
List DataRobot projects with optional filtering. Returns project metadata including name, stage, target, and modeling configuration.
manage_project
Update a project's name or delete a project entirely. Use action "update" to rename, or "delete" to soft-delete the project.
get_dataset
Retrieve detailed information about a specific dataset in the AI Catalog including its feature counts, size, and processing state.
get_model
Retrieve detailed information about a specific trained model including its type, metrics, training configuration, and optionally its feature impact scores.
list_prediction_environments
List prediction environment IDs and configuration available for deploying model packages.
download_dataset
Download the original persisted dataset as a CSV file. The dataset must permit data download.
download_model_artifact
Download a model training artifact or persistent logs as a tar.gz archive. Available for models built from blueprints with custom tasks.
request_feature_impact
Request feature impact computation for a trained model. Once computation finishes, use Get Model Details with includeFeatureImpact=true to retrieve scores.
list_prediction_servers
List available prediction server IDs and server metadata for deployment creation. Secret server keys are excluded.
get_model_package
Retrieve a deployment-ready registered model version, including build status, target, capabilities, and source model information.
create_batch_prediction_job
Score an AI Catalog dataset with a deployment in a batch job and retain CSV results for download. Poll Get Batch Prediction Job before downloading.
get_batch_prediction_job
Check batch prediction status, progress, scored rows, errors, and output configuration.
download_batch_predictions
Download a completed batch prediction job as a CSV file. The job must use localFile output.
get_current_user
Get the user identity associated with the connected DataRobot API key.
list_blueprints
List model blueprints available for a project, including blueprint IDs and modeling tasks.
list_feature_lists
List the feature lists available in a project, including IDs and selected feature names.
Every client uses the same Magic MCP URL, and so does any other MCP client. Add it once and sign in with SSO.
{"mcpServers": {"metorial": {"url": "<your-magic-mcp-url>"}}}
claude mcp add --transport http metorial <your-magic-mcp-url>
codex mcp add metorial --url <your-magic-mcp-url>codex mcp login metorial
One governed layer for every app your agents reach, not a separate connector per tool.
Manage DataRobot projects, start Autopilot, inspect trained models, and manage AI Catalog datasets. Register and deploy model packages, make serverless and batch predictions, and monitor service health, accuracy, and drift. Discover deployment infrastructure and download datasets, scored CSV files, and custom-task artifacts.
Common questions about connecting Datarobot to AI agents with Metorial.