Firebase ML API

Name: Firebase ML API API
Brand: Firebase ML API
Availability: InStock

✓ Official Vendor SpecAI/MLMl Inferenceoauth23 EndpointsREST

For Agents

Track and clean up long-running Firebase ML model management operations so an agent can supervise custom model upload pipelines.

Quickstart

Get started with Firebase ML 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:
"monitor firebase ml operations"

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

Capabilities

What an agent can do with Firebase ML API API.

List long-running Firebase ML operations under a project

Cancel a Firebase ML custom model upload or publish operation

Delete a completed Firebase ML operation resource

Monitor state of asynchronous Firebase ML model jobs from a pipeline

Reconcile Firebase ML operation history during model release reviews

GET STARTED

Start building with Firebase ML API API

Explore with Jentic

View OpenAPI Document

Use Cases

Patterns agents use Firebase ML API API for, with concrete tasks.

★ Custom Model Upload Monitoring

When a Firebase ML pipeline uploads a TensorFlow Lite model to Firebase for distribution to mobile clients, the upload runs as a long-running operation. The Firebase ML v1 API lets release tooling list and inspect those operations to confirm a model has finished processing before promoting it to production via the Firebase ML SDK.

Call GET /v1/{+name} on a Firebase ML operation resource and verify done=true with no error before announcing the new model version.

Cancelling a Stuck Model Operation

If a Firebase ML model upload or publish operation hangs because of an invalid model artifact or expired credential, POST /v1/{+name}:cancel on the operation resource cancels the job. This frees the resource so the pipeline can retry the upload with a corrected artifact.

Call POST /v1/{+name}:cancel on the in-flight Firebase ML operation and confirm via GET /v1/{+name} that done=true with cancellation reported in the status.

Firebase ML Operation Hygiene

Old Firebase ML operation resources accumulate over time as models are repeatedly uploaded and updated. DELETE /v1/{+name} removes a completed operation record so monitoring views remain focused on current activity. Run as a low-frequency maintenance task alongside model release reviews.

Call DELETE /v1/{+name} on a Firebase ML operation that has been done for over 60 days to keep operation listings focused on recent activity.

Agent-Driven Custom Model Release via Jentic

An AI agent connected through Jentic can supervise the upload-and-publish lifecycle of a Firebase ML custom model on behalf of an ML engineer, polling operations to confirm successful processing and cancelling failed jobs. Jentic exposes the 3 Firebase ML v1 endpoints, the agent strings them into a watch-cancel-cleanup workflow, and OAuth 2.0 credentials stay inside the Jentic vault.

Through Jentic, search for monitor firebase ml operations, load the operations.list endpoint, and execute it under projects/PROJECT to flag any operations whose done is still false after 30 minutes.

Key Endpoints

3 endpoints — the firebase ml api provides programmatic access to long-running operations spawned by firebase ml model management workflows, such as uploading and publishing custom machine learning models for use by firebase mobile apps.

METHOD

PATH

DESCRIPTION

GET

/v1/{+name}

List Firebase ML operations under a project

POST

/v1/{+name}:cancel

Cancel a Firebase ML long-running operation

DELETE

/v1/{+name}

Delete a Firebase ML operation resource

GET

/v1/{+name}

List Firebase ML operations under a project

POST

/v1/{+name}:cancel

Cancel a Firebase ML long-running operation

DELETE

/v1/{+name}

Delete a Firebase ML operation resource

Why though Jentic?

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

Credential isolation

Google OAuth 2.0 service-account credentials are stored in the Jentic vault. Agents receive a scoped access token per call, so the underlying private key never enters the agent's prompt or tool-call payloads.

Intent-based discovery

Agents search Jentic with intents like monitor firebase ml operations and Jentic returns the operations.list, operations.cancel, and operations.delete endpoints along with their input schemas.

Time to first call

Direct integration with the Firebase ML operations API: 1-2 days for OAuth and pipeline polling. Through Jentic: under 30 minutes.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Complementary

Firebase Management API

Provisions the Firebase project that owns Firebase ML custom models.

Choose Firebase Management first to set up the project; use Firebase ML afterwards to manage custom model release operations.

Alternative

Google Cloud AI Platform Training and Prediction API

Hosts and serves trained models from Google Cloud rather than distributing them to mobile devices.

Choose Cloud AI Platform when inference happens server-side at scale; use Firebase ML when the model needs to ship to iOS or Android clients via the Firebase ML SDK.

Alternative

Google Cloud AutoML API

Trains custom models without code rather than only managing their distribution.

Choose AutoML when the agent needs to train a custom model from labelled data; use Firebase ML to manage the upload-and-publish lifecycle of an already-trained on-device model.

FAQs

Specific to using Firebase ML API API through Jentic.

What authentication does the Firebase ML API use?

The API uses Google OAuth 2.0 with the firebase or cloud-platform scope. Through Jentic the OAuth credentials are stored in the encrypted vault and a scoped access token is supplied per request without exposing the underlying service-account key.

Can I run model inference with the v1 Firebase ML API endpoints exposed here?

No. The v1 surface in this spec covers long-running operation management only. Inference on Firebase ML custom models happens on-device through the Firebase ML SDKs after a model has been uploaded and published, not through this REST API.

What are the rate limits for the Firebase ML API?

Google enforces standard googleapis.com per-project quotas for the Firebase ML management endpoints. Operation listing and cancel are low-cost calls; pipelines that poll frequently should use exponential backoff to stay within per-minute quotas.

How do I monitor a Firebase ML model upload operation through Jentic?

Run pip install jentic, search for monitor firebase ml operations, load the operations.list endpoint, and execute it under projects/PROJECT to find operations whose done is false. Sign up at https://app.jentic.com/sign-up.

Does the Firebase ML API support cancelling a stuck custom model upload?

Yes. POST /v1/{+name}:cancel on the operation resource cancels the in-flight upload or publish job and reports a cancelled status when complete, letting release pipelines retry with a fixed model artifact.

Is the Firebase ML API free?

Firebase ML custom model hosting is included with Firebase under standard limits, and the operations endpoints themselves do not incur a separate per-call charge. Model storage and download bandwidth fall under standard Firebase project usage.