Google Firebase ML API
Browse all Google APIs38
AI ReadinessNon-Ready (F)38/100
Official vendor OpenAPI document · agent-readyAI/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.
Use for: List Firebase ML operations under a project, Cancel a stuck Firebase ML model upload operation, Check whether a Firebase ML operation has finished, Find all completed Firebase ML operations to prune
Not supported: Does not train models, perform on-device inference, or upload model artifacts directly via this v1 spec - use for managing Firebase ML long-running operations only.
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. The v1 surface exposes operations to list, cancel, and delete those operations. Use it to monitor model upload pipelines and manage cleanup of operation history during model publishing automation.
Install Jentic One Beta
Connect the Firebase ML API to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Firebase ML API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.
Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.
1
Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fgoogleapis.com%2Ffirebaseml" | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fgoogleapis.com%2Ffirebaseml" | sh
jentic register # connects your agent to your Jentic One instanceJentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.
Capabilities
What an agent can do with Firebase ML 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
Use Cases
Patterns agents use Firebase ML 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 your Jentic One instance.
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
Jentic AI Readiness Score
This API is usable in Jentic One now. Its AI-readiness score against Jentic's framework shows where it stands today and where improvements would make it even easier for agents to use.
F
Firebase ML API
- Non-Ready (F)38/100
73
Foundational Compliance56
Developer Experience & Jentic Compatibility16
AI-Readiness & Agent Experience94
Agent Usability63
Security63
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.0
Show dimension breakdown
73
Foundational Compliance
Base layer of spec validity and structural soundness.
Grade: A-Signals: 4
90%
Lint Results
Aggregated quality score from linter diagnostics, weighted by severity.
100%
Resolution Completeness
Percentage of `$ref` references that resolve successfully.
0%
Specification Validity
Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).
100%
Structural Integrity
Structural correctness score based on schema issues using logarithmic dampening.
56
Developer Experience & Jentic Compatibility
Clarity, completeness, and ingestion readiness for developers and tooling.
Grade: CSignals: 4
0%
Example Density
How richly the API is illustrated with examples.
100%
Example Validity
Percentage of examples that conform to their schemas.
25%
Response Coverage
Percentage of operations with complete response definitions (success, client error, server error).
100%
Tooling Readiness
Health of API ingestion, bundling, and resolution within Jentic pipelines.
16
AI-Readiness & Agent Experience
Semantic breadth, depth, and agent comprehension for AI systems.
Grade: FSignals: 4
64%
Description Coverage
Coverage of descriptions across API elements.
0%
Error Standardization
Coverage of RFC 9457 Problem Details for error responses.
0%
OperationId Quality
Coverage, uniqueness, and casing consistency of operationIds for AI inference.
0%
Summary Coverage
Coverage of summaries across operations/tags/info.
94
Agent Usability
Functional utility, complexity comfort, and AI orchestration readiness.
Grade: A+Signals: 1
94%
Complexity Comfort
Agent comfort level based on API operational and structural complexity.
63
Security
Trust, risk posture, and security compliance.
Grade: B-Signals: 1
62%
Authentication Strength
Average quality of security schemes based on authentication method strength (weakest link for OAuth2).
63
AI Discoverability
Findability, semantic richness, and reasoning readiness.
Grade: B-Signals: 1
62%
Descriptive Richness
Clarity and depth of descriptions across API elements.
Score it yourself
Every API in the directory is allowlisted, so you can re-score it with no key required.
npx @jentic/api-scorecard-cli score <openapi-url>Why Jentic?
What agents get from Jentic-routed access to this vendor.
Setup
Wiring the Firebase ML API by hand means setting up Google OAuth 2.0 with a service account and polling the long-running operation resources to track, cancel, and clean them up. Through Jentic you install once, import the Firebase ML API from the API Directory, store the service-account credential once, and your agent calls it.
Permission scoping
This v1 surface acts on operation resources named in the URL path (/v1/{name}, /v1/{name}:cancel), so a rule can pin your agent to one operation and nothing else. You choose the operations it may call, so ones like cancel or delete are not included unless you add them.
Credential isolation
Your Google service-account credential is stored once, encrypted, by your own Jentic One instance and injected at execution time. It never enters the agent's prompt, logs, or context.
Intent-based discovery
Agents search Jentic by intent such as 'monitor a Firebase ML operation' or 'cancel a pending operation', and Jentic returns the matching operation with its input schema so the agent calls the right endpoint without browsing the reference docs.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Firebase ML 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. Get started with Jentic One, the self-hosted execution layer.
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.
Can I limit what my agent is allowed to do with the Firebase ML API?
Yes. Because you run Jentic One yourself, your own rules decide which of the three Firebase ML v1 operations the agent may call, so read-only listing at GET /v1/{name} can be permitted while POST /v1/{name}:cancel and DELETE /v1/{name} stay excluded unless you add them. Since each call acts on an operation resource named in the URL path, a rule can pin the agent to one specific operation and nothing else. The Google OAuth 2.0 credential is stored by your instance and injected only when an allowed operation runs.
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