Ultralytics Platform MCP

List datasets in your Ultralytics workspace.

Get details for one deployment by owner/deployment or a bare slug (owner defaults to the account owner). serviceUrl and deployedAt are null until the deployment reaches status ready.

Probe one deployment's health by owner/deployment or a bare slug (owner defaults to the account owner). status is the upstream HTTP status the health probe observed at the deployment's own service URL, not the status of this tool call.

Read log entries for one deployment by owner/deployment or a bare slug (owner defaults to the account owner). severity is passed through to the server unvalidated (illustrative values: DEBUG, INFO, NOTICE, WARNING, ERROR, CRITICAL, ALERT, EMERGENCY); an invalid value returns the server's own rejection message. limit defaults to 50, max 200. nextPageToken pages through older entries.

Read metrics for one deployment by owner/deployment or a bare slug (owner defaults to the account owner). The response is one of two shapes selected by sparkline: the default shape carries timeRange (a {start, end} object)/summary/timeSeries, sparkline=true carries requests24h (an array of per-hour points, not a total)/totalRequests/errorRate/avgLatencyMs. The two are never merged; which shape came back is returned as-is. range (e.g. 1h, 24h, 7d; default 24h) is passed through to the server unvalidated.

Run inference through a deployment's own serving endpoint on a local image or video file, by owner/deployment or a bare slug (owner defaults to the account owner). Returns images/metadata verbatim, including undocumented metadata fields. No per-request cost is documented for this endpoint; costs follow the deployment's own resource configuration. A cold start on a scaled-to-zero deployment may respond slowly or with a 503 — check deployment_health rather than retrying blindly.

Stop a deployment by owner/deployment or a bare slug (owner defaults to the account owner). Sends only {action: stop}; the endpoint's start/resize/replace actions are unreachable from this tool. Stopping preserves the deployment's URL and configuration and still counts toward deployment quota; it is a money-off switch and ships ungated, consistent with training_cancel and export_cancel. Stopping an already-stopped deployment is rejected by the server (400) rather than treated as a success. Reversing this (start) is not available in this tool set.

List model deployments in your Ultralytics workspace.

Search public datasets on Ultralytics Explore. Matching is currently loose: results need not contain the search term, so even an unrelated query returns datasets.

Search public projects on Ultralytics Explore.

Cancel an active export job for a model by owner/project/model, ul://owner/project/model, or slug with a project, plus the export id. Checks the export's status first and sends the cancellation only while it is still active, refusing on any terminal status; the same API verb deletes a finished export's artifact irreversibly instead of cancelling it. The status check cannot be atomic: an export that finishes between the check and the request will have its artifact deleted rather than cancelled, and that deletion cannot be undone.

Create a model export job by owner/project/model, ul://owner/project/model, or slug with a project (state-changing, may cost credits). The format is validated immediately by the server; task and architecture compatibility is only known when the job runs, so a queued export can still fail. Use export_status and exports_list for the real outcome. Requires confirm_cost=true.

Get one export job's status for a model by owner/project/model, ul://owner/project/model, or slug with a project, plus the export id.

List export jobs for a model by owner/project/model, ul://owner/project/model, or slug with a project.

Get current cloud-GPU stock status by GPU type.

Download a trained model's weight file to an explicit local path by owner/project/model, ul://owner/project/model, or slug with a project.

Report a model's best training epoch and its reported metrics, labelled so neither can be mistaken for the other (works for private and public projects). bestEpoch, bestFitness, and bestEpochMetrics are stated only when exactly one trainResults record has the reported bestEpoch and that record's fitness exactly equals the reported bestFitness; otherwise all three are null and bestEpochNote keeps the platform's raw values and says why (older Ultralytics versions reported the last epoch as bestEpoch). reportedMetrics is the model's top-level metrics field verbatim; the API does not say which eva…

Report a model's evaluation plots (per-class pr_curve, f1_curve, precision_curve, recall_curve, confusion_matrix), which model_metrics and training_monitor do not surface. By default lists each plot's type and the shape of its fields (array lengths only, never the values) since one pr_curve alone can carry thousands of numbers on a multi-class model; pass type to get that one plot's data back exactly as the platform returned it, unmodified. Field shapes vary by type: pr_curve/f1_curve/precision_curve/recall_curve carry x/y (and pr_curve additionally ap); confusion_matrix carries a matrix field…

Run inference with a trained model on an image URL or base64 image (`source`), or on a local image or video file (`file_path`). Give exactly one.

Soft-delete a model by owner/project/model, ul://owner/project/model, or slug with a project. Deleted models go to trash and remain restorable; weights, training history, and exports are removed only on permanent deletion.

Get details for one model by owner/project/model, ul://owner/project/model, or slug with a project. Model quality (best epoch and fitness) is not included; use model_metrics, which validates the platform's best-epoch fields.

List models in a project by slug, owner/slug, or project ul:// URI. Model quality is not included; use model_metrics, which validates the platform's best-epoch fields.

Create a project in your Ultralytics workspace. Defaults to private visibility (the platform defaults to public when visibility is omitted).

Soft-delete a project by slug, owner/slug, or project ul:// URI. Deleted projects land in trash and remain restorable for a bounded window.

Get details for one project by slug, owner/slug, or project ul:// URI.

List computer-vision projects in your Ultralytics workspace.

Cancel a running training job by owner/project/model, ul://owner/project/model, or slug with a project. Cancelling releases the compute instance; elapsed GPU time is still charged and the most recently uploaded checkpoint is preserved rather than discarded. This stops the job and does not delete the model.

Report a model's training status and progress (works for private and public projects). reportedCurrentEpoch, reportedProgressPercentage, and etaMs are the training job's own counters, passed through verbatim; after a run ends reportedCurrentEpoch can count a post-training evaluation as an epoch, so it may be one higher than the epochs actually trained, with reportedProgressPercentage inflated to match, and etaMs can stay nonzero. With no training job, progress is reported unavailable rather than inferred. resultRecordCount counts trainResults records, which are not epochs: a history can includ…

Start a cloud training job from an existing model or official YOLO base checkpoint (state-changing, may cost credits). Trains on exactly one dataset and requires epochs. The dataset is validated immediately, so an unusable dataset is rejected before any compute starts. In checkpoint mode a project model is created first, then the checkpoint's task is checked against the dataset's task server-side when training starts, by which point the model already exists — a mismatch there still leaves that model behind, unrequested, and the error names it so it can be reviewed and deleted (models_delete) i…