Datarobot MCP server

Connect Datarobot to Claude, ChatGPT, Cursor, Claude Code, Codex, Gemini, GitHub Copilot and any other MCP client, for your whole team. Metorial adds security, governance, and tracing, and gives everyone one Magic MCP URL to connect.

What you can do with the Datarobot MCP server

Ask your agent in plain language. Each of these maps to a real tool on the server.

  • Use the get project tool in Datarobot
  • Use the list datasets tool in Datarobot
  • Use the get deployment monitoring tool in Datarobot
  • Use the make predictions tool in Datarobot
  • Use the start autopilot tool in Datarobot
  • Use the get deployment tool in Datarobot

Supported Tools

get_project

Get Project

Retrieve detailed information about a specific DataRobot project including its configuration, target, stage, partition settings, and advanced options.

list_datasets

List Datasets

List datasets in the DataRobot AI Catalog. Returns metadata including name, size, row/column counts, and processing state.

get_deployment_monitoring

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 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

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

Get Deployment

Retrieve detailed information about a specific model deployment including its health status, model information, capabilities, and configuration.

list_deployments

List Deployments

List all model deployments with their health status, importance, and prediction usage. Useful for monitoring deployed models across the organization.

create_deployment

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

Create, Update, or Delete 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 Models

List trained models in a DataRobot project. Returns model types, training details, and performance metrics from the leaderboard.

manage_deployment

Update, Replace Model, or Delete 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

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 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 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 Projects

List DataRobot projects with optional filtering. Returns project metadata including name, stage, target, and modeling configuration.

manage_project

Update or Delete 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

Get Dataset

Retrieve detailed information about a specific dataset in the AI Catalog including its feature counts, size, and processing state.

get_model

Get Model Details

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 Environments

List prediction environment IDs and configuration available for deploying model packages.

download_dataset

Download Dataset

Download the original persisted dataset as a CSV file. The dataset must permit data download.

download_model_artifact

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

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 Prediction Servers

List available prediction server IDs and server metadata for deployment creation. Secret server keys are excluded.

get_model_package

Get Model Package

Retrieve a deployment-ready registered model version, including build status, target, capabilities, and source model information.

create_batch_prediction_job

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

Get Batch Prediction Job

Check batch prediction status, progress, scored rows, errors, and output configuration.

download_batch_predictions

Download Batch Predictions

Download a completed batch prediction job as a CSV file. The job must use localFile output.

get_current_user

Get Current User

Get the user identity associated with the connected DataRobot API key.

list_blueprints

List Blueprints

List model blueprints available for a project, including blueprint IDs and modeling tasks.

list_feature_lists

List Feature Lists

List the feature lists available in a project, including IDs and selected feature names.

Connect Datarobot to your AI client

Every client uses the same Magic MCP URL, and so does any other MCP client. Add it once and sign in with SSO.

  1. Enable Datarobot in Metorial. In your Metorial portal, enable the Datarobot MCP server and sign in with the account that owns the data. Access stays governed by your team policies.
  2. Add a custom connector. Open Customize, then Connectors. Select Add, then Custom, then Web.
  3. Paste your Metorial MCP URL. Name it Metorial, paste your Magic MCP URL (<your-magic-mcp-url>) and continue.
  4. Sign in. Select Add and sign in to load Datarobot tools.

Run Datarobot through the Metorial gateway

One governed layer for every app your agents reach, not a separate connector per tool.

  • Give Datarobot and every other app to your whole team through one MCP gateway, behind your SSO.
  • Scope which teams and agents can use Datarobot tools with role-based access control, and trace every call.
  • Prefer to run it yourself? Metorial is open source and self-hostable.

More integrations teams use with Datarobot

Google Sheets

Create, read, and update spreadsheets and their cell data. Read and write values to individual cells, ranges, or multiple ranges using A1 notation or named ranges. Apply cell formatting including text styles, backgrounds, borders, number formats, and conditional formatting. Manage sheets (tabs) within a spreadsheet—add, delete, copy, rename, and reorder them. Create and manage embedded charts, pivot tables, filter views, data validation rules, protected ranges, named ranges, and merged cells. Perform batch operations to apply multiple updates atomically.

GitHub

Manage repositories, issues, and pull requests. Create and configure branches, star repositories, review code, and merge changes. Automate CI/CD workflows with GitHub Actions, manage workflow runs, secrets, and artifacts. Track issues with labels, milestones, and assignees. Search across code, repositories, issues, and users. Manage organizations, teams, and memberships. Create and manage projects, gists, packages, deployments, and environments. Access security alerts including code scanning, secret scanning, and Dependabot alerts. Read and write file contents in repositories. Manage webhooks, notifications, and codespaces.

Salesforce

Manage CRM data including Accounts, Contacts, Leads, Opportunities, Cases, and custom objects. Create, read, update, and delete records. Query data using SOQL and search across objects using SOSL. Perform bulk data operations for large-scale imports, exports, and migrations. Execute composite requests to batch multiple operations in a single API call. Access analytics, reports, and dashboards. Manage files and attachments associated with records. Interact with Chatter feeds, posts, and groups for social collaboration. Manage org metadata including custom objects, fields, layouts, and workflows. Query data using GraphQL for precise data retrieval across related objects.

Sharepoint

Manage SharePoint sites, document libraries, lists, and files. Create, read, update, and delete lists and list items with custom columns. Resolve site users to numeric Person/Group LookupId values. Upload, download, move, copy, and version files in document libraries. Search across sites, files, folders, lists, and list items using Microsoft Search. Manage permissions at site, list, and item levels with granular access control. Define and manage content types and site columns. Retrieve site properties and search for sites across Microsoft 365.

Airtable

Create, read, update, and delete records in Airtable bases and tables. Manage base schemas including creating tables and fields. Filter records using formulas, sort by fields, and scope queries to specific views. Upsert records to find, create, or update in a single call. Upload attachments to records, read and write record comments, list accessible bases, and receive real-time base change events through webhooks.

Google Drive

Upload, download, create, copy, move, rename, trash, and permanently delete files and folders in Google Drive. Search for files using complex queries filtering by name, MIME type, owner, modification date, labels, and other metadata. Share files and folders with specific users, groups, or domains with role-based permissions (owner, writer, commenter, reader). Manage shared drives and their members. Export Google Workspace files (Docs, Sheets, Slides) to standard formats like PDF, DOCX, and XLSX. Track file revision history and restore earlier versions. Create, read, update, and delete threaded comments and replies on files. Read label definitions and search files by label. Browse the Drive apps installed for your account. Store app-specific data in a hidden per-user folder.

Technical notes for Datarobot

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.

Frequently asked questions

Common questions about connecting Datarobot to AI agents with Metorial.

  1. How many tools does the Datarobot MCP server have?
    The Datarobot MCP server exposes 30 tools, including Get Project, List Datasets, Get Deployment Monitoring. Each one is governed and traced through Metorial.
  2. Datarobot exposes tools your agents call on demand; it does not publish event triggers yet. Everything still runs through one governed MCP URL.
  3. Any MCP client, including VS Code, Windsurf, Zed, OpenCode and Grok. Each one reaches the Datarobot MCP server through the same Metorial URL.
  4. Use role-based access control to decide which teams, users, and agents can use Datarobot and which of its tools they can call.
  5. Yes. Metorial is open source, so you can run the Datarobot MCP server yourself or use the hosted gateway.

Connect Datarobot to production AI agents

See how Metorial gives your team governed access to Datarobot, with tracing and security controls built in.