Amazon Lookout for Metrics

Name: Amazon Lookout for Metrics API
Brand: Amazon Lookout for Metrics
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLMl Inferencehmac30 EndpointsREST

For Agents

Detect anomalies in metric time series, manage anomaly detectors, and route findings to alert channels for automated response.

Quickstart

Get started with Amazon Lookout for Metrics 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:
"detect a metric anomaly"

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

Capabilities

What an agent can do with Amazon Lookout for Metrics API.

Create anomaly detectors that monitor metric sets sourced from S3, Redshift, CloudWatch, or RDS

Activate, deactivate, or back-test anomaly detectors against historical metric data

Configure metric sets with measure and dimension fields for grouped anomaly analysis

Surface anomaly groups ranked by severity and inspect contributing dimensions

GET STARTED

Start building with Amazon Lookout for Metrics API

Explore with Jentic

View OpenAPI Document

Jentic publishes the only available OpenAPI document for Amazon Lookout for Metrics, keeping it validated and agent-ready.

Jentic publishes the only available OpenAPI specification for Amazon Lookout for Metrics, keeping it validated and agent-ready. Amazon Lookout for Metrics uses machine learning to detect anomalies in business and operational metrics — sales drops, conversion changes, traffic spikes — without requiring data science expertise. Anomaly detectors ingest data from sources such as Amazon S3, Amazon Redshift, Amazon CloudWatch, and Amazon RDS, then surface deviations along with severity scores and grouped contributing dimensions. Alerts route findings to channels like Amazon SNS or AWS Lambda so downstream systems can respond automatically.

Use Cases

Patterns agents use Amazon Lookout for Metrics API for, with concrete tasks.

★ Revenue and Conversion Anomaly Detection

E-commerce and SaaS teams use Lookout for Metrics to spot unexpected drops in revenue, sign-ups, or conversion rates without writing forecasting code. The service builds models per metric set, learns seasonality, and surfaces anomalies grouped by contributing dimensions such as product, region, or channel. Setup is point-and-click against an S3 or Redshift data source and produces results within hours of activation.

Create an anomaly detector for daily revenue with a one-day frequency, attach an S3-backed metric set with dimensions product_category and region, and activate the detector

Operational Telemetry Monitoring

DevOps and SRE teams attach Lookout for Metrics to CloudWatch metrics to find anomalies in latency, error rates, and throughput across services. Detectors group anomalies by service or instance dimension, helping engineers locate root causes faster than fixed-threshold alarms. The CloudWatch source connector ingests metrics directly so no ETL is required.

Configure a metric set sourced from CloudWatch latency metrics, attach it to an existing detector, and create an alert that triggers a Lambda function for anomalies above severity 70

Marketing Campaign Performance Tracking

Marketing analytics teams monitor click-through rates, ad spend, and acquisition costs across campaigns. Lookout for Metrics groups anomalies by campaign and channel dimensions, flagging when a single ad creative collapses while overall metrics stay healthy. Feedback loops let analysts mark false positives so the model adapts.

List anomaly groups for the marketing detector created in the last 7 days, then post user feedback marking the top result as anomaly type relevant

AI Agent Anomaly Triage Workflows

AI agents call Lookout for Metrics through Jentic to retrieve fresh anomaly groups, fetch contributing dimensions, and decide whether to open a ticket, page an on-call engineer, or annotate a dashboard. Jentic isolates the AWS Signature v4 credentials and exposes the detector and metric-set operations as discoverable tools so the agent can act without learning the SDK.

Search Jentic for 'detect a metric anomaly', load the ListAnomalyGroupSummaries operation for detector arn:aws:lookoutmetrics:..., and execute it for the last 6 hours

Key Endpoints

30 endpoints — jentic publishes the only available openapi specification for amazon lookout for metrics, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

POST

/CreateAnomalyDetector

Create a new anomaly detector

POST

/CreateMetricSet

Attach a metric set to a detector

POST

/ActivateAnomalyDetector

Activate a detector to begin training and ingestion

POST

/BackTestAnomalyDetector

Run a detector against historical data

POST

/ListAnomalyGroupSummaries

List detected anomaly groups for a detector

POST

/CreateAlert

Create an alert that routes anomalies to SNS or Lambda

POST

/PutFeedback

Submit human feedback on a detected anomaly

POST

/CreateAnomalyDetector

Create a new anomaly detector

POST

/CreateMetricSet

Attach a metric set to a detector

POST

/ActivateAnomalyDetector

Activate a detector to begin training and ingestion

POST

/BackTestAnomalyDetector

Run a detector against historical data

POST

/ListAnomalyGroupSummaries

List detected anomaly groups for a detector

Why though Jentic?

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

Credential isolation

AWS access key ID and secret access key are stored encrypted in the Jentic vault. Agents receive a scoped session — Jentic signs each request with Signature Version 4 so raw long-lived keys never enter the agent's context.

Intent-based discovery

Agents search by intent (e.g. 'detect a metric anomaly') and Jentic returns matching Lookout for Metrics operations such as ListAnomalyGroupSummaries, with their input schemas, so the agent can call the right endpoint without browsing the AWS docs.

Time to first call

Direct integration: 1-2 days to wire up Signature v4, IAM policies, and SDK error handling. Through Jentic: under an hour — search, load schema, execute.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Complementary

Amazon CloudWatch

CloudWatch is the metric source many Lookout detectors ingest from

Choose CloudWatch when the agent needs to publish or query raw metrics; use Lookout for Metrics when it needs to detect anomalies on top of those metrics.

Alternative

Amazon Forecast Query

Forecast produces probabilistic forecasts; Lookout detects anomalies after the fact

Choose Forecast Query when the agent needs predicted values for future periods; choose Lookout for Metrics when the agent needs to flag deviations in observed data.

Alternative

Amazon SageMaker

SageMaker offers full custom ML model training; Lookout is a managed anomaly service

Choose SageMaker when the agent needs to train and deploy custom anomaly models; choose Lookout for Metrics when a managed, no-code detector is sufficient.

FAQs

Specific to using Amazon Lookout for Metrics API through Jentic.

Why is there no official OpenAPI spec for Amazon Lookout for Metrics?

AWS does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Amazon Lookout for Metrics via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.

What authentication does the Amazon Lookout for Metrics API use?

The API uses AWS HMAC request signing (Signature Version 4) with an access key ID and secret access key scoped via IAM. Through Jentic, AWS credentials are stored encrypted in the vault and never enter the agent's prompt context — the agent receives a scoped session that signs each call.

Can I create and activate an anomaly detector with the Amazon Lookout for Metrics API?

Yes. POST /CreateAnomalyDetector defines a detector with frequency and KMS settings, POST /CreateMetricSet attaches the data source and metric definitions, and POST /ActivateAnomalyDetector starts model training and ingestion. Back-testing against historical data is available via POST /BackTestAnomalyDetector.

What are the rate limits for the Amazon Lookout for Metrics API?

AWS does not document hard request-per-second limits for Lookout for Metrics control-plane endpoints in the public spec. Quotas apply to detectors per account, metric sets per detector, and ingested data volume — see the AWS service quotas console for current values for your region.

How do I retrieve recent anomaly groups through Jentic?

Search Jentic for 'list anomaly groups' to discover POST /ListAnomalyGroupSummaries, load its schema with the Jentic Python SDK (pip install jentic), and execute it with the detector ARN and the desired time range. The MCP tool name is aws_list_anomaly_groups.

Is Amazon Lookout for Metrics free?

No. Lookout for Metrics charges per metric analyzed per month with a free tier for new accounts in the first month. Detector creation and metric-set configuration are free; you pay for the metrics actively monitored. See the AWS pricing page for the current rate per metric.