Google Recommender API
Browse all Google APIs41
AI ReadinessFoundational (D-)41/100
Official vendor OpenAPI document · agent-readyCloud InfrastructureComputeoauth212 EndpointsREST
For Agents
Fetch Google Cloud cost, security, and performance recommendations for projects and folders, and update each recommendation's lifecycle state as agents act on them.
Use for: List all idle VM recommendations in my Google Cloud project, Get the cost-saving recommendations for the production folder, Mark a recommendation as accepted after the underlying change has been applied, Find IAM rightsizing recommendations across all locations in this organization
Not supported: Does not apply changes to underlying Google Cloud resources, surface billing invoices, or provision new infrastructure - use for retrieving and updating the lifecycle of recommendations and insights only.
The Google Cloud Recommender API surfaces machine-learning-driven recommendations and insights about Google Cloud resources, helping teams optimize cost, security, performance, and reliability across projects, folders, and organizations. It exposes recommenders for areas such as idle VM detection, IAM role rightsizing, and commitment utilization, and lets clients mark recommendations as claimed, accepted, succeeded, failed, or dismissed to track lifecycle. Insights and recommendations are scoped per location and recommender type, with full IAM-controlled access and audit visibility.
Install Jentic One Beta
Connect the Recommender API to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Recommender 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%2Frecommender" | 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%2Frecommender" | 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 Recommender API.
List active recommendations for a project, folder, or organization filtered by recommender type and location
Mark a recommendation as claimed, accepted, succeeded, failed, or dismissed to track agent-driven remediation
Retrieve insights that explain the underlying signals behind a Google Cloud recommendation
Update recommender configuration to enable or disable specific recommendation types per scope
Enumerate available recommenders and insight types so agents can discover what optimization signals exist
Report whether an insight has been accepted by an operator or rejected to refine future recommendations
Use Cases
Patterns agents use Recommender API for, with concrete tasks.
★ Automated cost optimization sweep
Run a recurring agent that lists idle VM and rightsizing recommendations across every project in an organization, applies the suggested change through the Compute Engine API, and then marks each Recommender recommendation as succeeded so it stops appearing in dashboards. Combining listing, IAM-scoped filtering, and lifecycle marking lets cloud teams reclaim spend without manual review.
List recommendations under projects/my-prod/locations/us-central1/recommenders/google.compute.instance.IdleResourceRecommender, apply each, then call markSucceeded on every applied recommendation.
Continuous IAM rightsizing
An agent enumerates IAM policy recommendations under each project, evaluates the suggested role reductions against an internal allow-list, applies safe changes via the IAM API, and marks accepted recommendations claimed and then succeeded. This closes the loop between detection and remediation for least-privilege enforcement.
List google.iam.policy.Recommender recommendations for projects/finance-prod, mark each as CLAIMED, apply the role changes, and call markSucceeded on completion.
Security insight triage
Surface insights from security-focused recommenders into a triage queue, enrich them with the explanatory metadata returned from the insights endpoint, and let an agent dismiss low-value findings while escalating high-severity ones to a ticketing system. Each lifecycle change is recorded against the Recommender API so reporting stays consistent.
Iterate insights under projects/my-prod/locations/global/insightTypes/google.iam.policy.Insight, post HIGH severity items to PagerDuty, and call markAccepted on the rest.
Agent-driven cloud governance through Jentic
An AI agent built on Jentic discovers Recommender operations by intent, loads the schema for listing and lifecycle endpoints, and runs a governance loop that pulls recommendations, applies safe ones, and marks them succeeded. Credentials stay isolated in Jentic's vault so agents never see raw OAuth tokens.
Search Jentic for 'list google cloud recommendations', load the Recommender list schema, execute against projects/my-prod, then call markSucceeded for each handled recommendation.
Key Endpoints
12 endpoints — the google cloud recommender api surfaces machine-learning-driven recommendations and insights about google cloud resources, helping teams optimize cost, security, performance, and reliability across projects, folders, and organizations.
METHOD
PATH
DESCRIPTION
GET
/v1beta1/{+parent}/recommendations
List recommendations for a parent scope and recommender
GET
/v1beta1/{+parent}/insights
List insights for a parent scope and insight type
POST
/v1beta1/{+name}:markAccepted
Mark a recommendation as accepted
POST
/v1beta1/{+name}:markClaimed
Mark a recommendation as claimed by an actor
POST
/v1beta1/{+name}:markSucceeded
Mark a recommendation as succeeded after applying
POST
/v1beta1/{+name}:markDismissed
Dismiss a recommendation so it no longer appears
GET
/v1beta1/recommenders
List available recommender types
GET
/v1beta1/insightTypes
List available insight types
GET
/v1beta1/{+parent}/recommendations
List recommendations for a parent scope and recommender
GET
/v1beta1/{+parent}/insights
List insights for a parent scope and insight type
POST
/v1beta1/{+name}:markAccepted
Mark a recommendation as accepted
POST
/v1beta1/{+name}:markClaimed
Mark a recommendation as claimed by an actor
POST
/v1beta1/{+name}:markSucceeded
Mark a recommendation as succeeded after applying
POST
/v1beta1/{+name}:markDismissed
Dismiss a recommendation so it no longer appears
GET
/v1beta1/recommenders
List available recommender types
GET
/v1beta1/insightTypes
List available insight types
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.
R
Recommender API
- Foundational (D-)41/100
64
Foundational Compliance56
Developer Experience & Jentic Compatibility18
AI-Readiness & Agent Experience94
Agent Usability63
Security69
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.0
Show dimension breakdown
64
Foundational Compliance
Base layer of spec validity and structural soundness.
Grade: BSignals: 4
80%
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).
75%
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.
18
AI-Readiness & Agent Experience
Semantic breadth, depth, and agent comprehension for AI systems.
Grade: FSignals: 4
72%
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).
69
AI Discoverability
Findability, semantic richness, and reasoning readiness.
Grade: B+Signals: 1
69%
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 Recommender API by hand means setting up Google OAuth 2.0, refreshing scoped access tokens, and threading recommendation and insight resource names through the v1beta1 endpoints on recommender.googleapis.com. Through Jentic you install once, import the Recommender API from the API Directory, store the OAuth credential once, and your agent calls it.
Permission scoping
This API puts the recommendation and insight in the URL path (/v1beta1/{+name} and /v1beta1/{+parent}/recommendations), so a rule can pin your agent to one project's recommendations: it can list and read within that scope and nothing else. You choose the operations it may call, so state-changing ones like marking a recommendation accepted or dismissed are not included unless you add them.
Credential isolation
Your Google OAuth credential is stored once, encrypted, by your own Jentic One instance and a scoped access token is injected at execution time. The credential never enters the agent's prompt, logs, or context.
Intent-based discovery
Agents search Jentic by intent such as 'list Google Cloud recommendations' or 'mark a recommendation accepted', and Jentic returns the matching Recommender operation with its input schema so the agent calls the right endpoint without browsing the discovery document.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Recommender API through Jentic.
What authentication does the Recommender API use?
The Recommender API uses Google OAuth 2.0 with the cloud-platform scope, declared as the Oauth2 and Oauth2c security schemes in the spec. Through Jentic, OAuth tokens are stored encrypted in your Jentic One instance and exchanged at call time, so agents never receive raw refresh tokens or service account keys.
Can I mark recommendations as accepted or dismissed with the Recommender API?
Yes. The API exposes lifecycle endpoints at /v1beta1/{+name}:markAccepted, :markClaimed, :markSucceeded, :markFailed, and :markDismissed. Each call requires the etag from the most recent get to avoid concurrent updates, and the response contains the updated state record.
What are the rate limits for the Recommender API?
Google Cloud applies per-project quotas to the Recommender API; the standard default is 600 read requests per minute per project, with mutating mark calls counted against the same pool. Quotas can be increased through the Cloud Console quotas page once a project has demonstrated steady usage.
How do I list cost recommendations for a project through Jentic?
Search Jentic for 'list google cloud recommendations', load the schema for GET /v1beta1/{+parent}/recommendations, and execute with parent set to projects/PROJECT/locations/LOCATION/recommenders/google.compute.instance.IdleResourceRecommender. Jentic returns the parsed recommendation list with etags ready for follow-up mark calls.
Is the Recommender API free?
The Recommender API itself is free to call within Google Cloud quotas; you pay only for the underlying resources whose recommendations you act on, such as Compute Engine VMs or IAM-managed services. There is no per-recommendation charge for retrieval or lifecycle updates.
Which recommenders and insight types can I enumerate?
Calling GET /v1beta1/recommenders and GET /v1beta1/insightTypes returns the full registry, including google.compute.instance.IdleResourceRecommender, google.iam.policy.Recommender, and google.cloudbilling.commitment.SpendBasedCommitmentRecommender, among others. Use these identifiers in subsequent list calls.
Can I limit what my agent is allowed to do with the Recommender API?
Yes. Because Jentic One is self-hosted, your own rules decide which Recommender operations and credentials the agent may use. Since this API carries the project and recommendation names in the URL path, you can pin the agent to one project's recommendations so it only lists and reads within that scope. State-changing calls such as markAccepted or markDismissed are excluded unless you explicitly grant them.
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