Data Labeling API

Name: Data Labeling API API
Brand: Data Labeling API
Availability: InStock

✓ Official Vendor SpecAI/MLMl Inferenceoauth228 EndpointsREST

For Agents

Submit raw data to Google's Data Labeling Service, manage labeling jobs, and export human-labeled datasets for ML training. Lets agents drive a labeling pipeline without operating a workforce.

Quickstart

Get started with Data Labeling API in minutes using your preferred integration method.

# Add to your MCP client config (Claude Desktop, Cursor, Windsurf)
{
  "jentic": {
    "url": "https://api.jentic.com/mcp",
    "auth": "oauth"
  }
}

# Then ask your agent:
"submit an image labeling job"

# → Jentic returns the GET /events tool with parameter schema, agent executes.

Capabilities

What an agent can do with Data Labeling API API.

Create datasets and import images, video, or text from Cloud Storage

Submit image, video, and text labeling jobs to Google's annotation workforce

Track labeling job status, pause, resume, and cancel running jobs

Export annotated datasets to Cloud Storage in standard ML training formats

GET STARTED

Start building with Data Labeling API API

Explore with Jentic

View OpenAPI Document

Use for: I need to submit an image classification labeling job for 5,000 photos, Create a dataset and import images from a Cloud Storage bucket, Export the annotated dataset for training a Vertex AI model, List all labeling jobs that are currently in WAITING state

Not supported: Does not train ML models, run inference, or host model endpoints — use for dataset import, human-labeled annotation jobs, and labeled-data export only.

Use Cases

Patterns agents use Data Labeling API API for, with concrete tasks.

★ Bootstrap a Supervised Training Dataset

Submit raw images, video, or text from Cloud Storage and receive a fully annotated dataset back, ready to train a Vertex AI or custom TensorFlow model. The API handles importData, requesting a labeling job with an annotation spec, and exportData when the job completes. End-to-end turnaround is typically days rather than the weeks needed to operate an in-house annotation team.

Create a dataset 'cat-dog-photos', import images from gs://acme-ml/raw/, submit an image classification labeling job with an annotation spec of CAT and DOG labels, and export the result when the job completes

Active Learning Loop with Human Labels

Use the Data Labeling Service inside an active learning loop: a model identifies low-confidence predictions, those examples are submitted as a new labeling job, and the resulting AnnotatedDataset feeds back into model retraining. The API's pause/resume/cancel controls let the loop adjust labeling instructions or stop early if the model's accuracy plateaus.

Submit a labeling job for 500 low-confidence predictions exported by the previous training run, monitor the operation, and merge the new AnnotatedDataset into the existing dataset

Audit and Export Labeled Datasets

Inspect existing annotated datasets to verify label coverage, sample examples, and confirm annotation spec consistency before exporting. The API exposes list and get operations on AnnotatedDatasets, Examples, and AnnotationSpecs, plus an exportData operation that writes results back to Cloud Storage in JSONL format. This supports compliance reviews and dataset versioning workflows.

List all AnnotatedDatasets under dataset 'cat-dog-photos', sample 10 examples from each, and exportData of the most recent annotated dataset to gs://acme-ml/labeled/v3/

AI Agent Labeling Operator

An AI agent can drive the full label-train-evaluate loop through Jentic without operator intervention. Jentic search returns the right Data Labeling operation by intent, the agent loads the schema, and Jentic executes against datalabeling.googleapis.com using vault-stored credentials. This collapses a multi-day setup into a single agent run.

Use Jentic to search 'request an image labeling job', load the labelImage schema, and execute it for dataset 'cat-dog-photos' with classification config and basic-config containing the instruction URI

Key Endpoints

28 endpoints — the google cloud data labeling api exposes the public surface of the data labeling service for ai training data preparation.

METHOD

PATH

DESCRIPTION

POST

/v1beta1/{+parent}/datasets

Create a labeling dataset

POST

/v1beta1/{+name}:importData

Import raw data from Cloud Storage

POST

/v1beta1/{+parent}/annotationSpecSets

Create an annotation spec set

POST

/v1beta1/{+name}:exportData

Export an annotated dataset to Cloud Storage

POST

/v1beta1/{+name}:pause

Pause a running labeling job

POST

/v1beta1/{+name}:resume

Resume a paused labeling job

POST

/v1beta1/{+parent}/datasets

Create a labeling dataset

POST

/v1beta1/{+name}:importData

Import raw data from Cloud Storage

POST

/v1beta1/{+parent}/annotationSpecSets

Create an annotation spec set

POST

/v1beta1/{+name}:exportData

Export an annotated dataset to Cloud Storage

POST

/v1beta1/{+name}:pause

Pause a running labeling job

Why though Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

Google OAuth client secrets and refresh tokens are stored encrypted in the Jentic vault. Agents receive scoped access tokens for datalabeling.googleapis.com; raw client credentials never enter the agent context.

Intent-based discovery

Agents search Jentic by intent (e.g. 'submit an image labeling job') and Jentic returns the matching labelImage, labelVideo, or labelText operation with its input schema.

Time to first call

Direct Data Labeling integration: 2-4 days for OAuth, long-running-operation handling, and labeling-config schema work. Through Jentic: under 1 hour.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Complementary

Cloud AutoML API

AutoML trains models on the labeled datasets that the Data Labeling Service produces.

Choose AutoML once the agent has a labeled dataset and needs to train a model. Use Data Labeling to produce that dataset.

Alternative

Cloud Vision API

Vision provides pretrained image classification and detection; Data Labeling produces custom labels for custom training.

Choose Vision when pretrained labels suffice. Use Data Labeling when the domain needs custom annotations.

Complementary

Cloud Storage API

Data Labeling reads input and writes exports to Cloud Storage; Storage holds the raw and labeled assets.

Choose Cloud Storage for the agent's bucket, object, and ACL operations. Use Data Labeling for the annotation workflow.

FAQs

Specific to using Data Labeling API API through Jentic.

What authentication does the Data Labeling API use?

The Data Labeling API uses OAuth 2.0 with the cloud-platform scope. Through Jentic the OAuth client and refresh tokens are stored in the Jentic vault and the agent receives short-lived scoped tokens, so raw Google credentials never enter the agent context.

Can I label images, video, and text with the Data Labeling API?

Yes. The API exposes labelImage, labelVideo, and labelText operations under the dataset resource, each accepting a labeling configuration (classification, bounding box, segmentation, entity extraction, sentiment) plus the annotation spec set and instruction URI. All three submit jobs to the same managed annotation workforce.

What are the rate limits for the Data Labeling API?

Google enforces standard Cloud quotas: per-project rate limits on read/write calls and a quota on concurrent labeling jobs per project. Quotas are visible in the Cloud Console under IAM and admin, quotas, filtered to datalabeling.googleapis.com.

How do I export a labeled dataset through Jentic?

Search Jentic for 'export an annotated dataset', load the schema for POST /v1beta1/{+name}:exportData, and execute with the AnnotatedDataset resource name and an outputConfig pointing at a Cloud Storage URI. Jentic returns the long-running operation name to poll.

Is the Data Labeling API free?

The API itself is free; Google charges per-unit fees for human-labeled examples (per image, per video minute, per text segment), with separate tier pricing for tier-1 versus tier-2 labelers. Cloud Storage costs apply to imported and exported data.

How do I pause a labeling job to fix instructions?

Call POST /v1beta1/{+name}:pause on the long-running operation associated with the labeling job. Update the Instruction document, then call POST /v1beta1/{+name}:resume to continue. Cancelled jobs cannot be resumed; only paused jobs can.