Microsoft Azure Azure Anomaly Finder Client
Browse all Azure APIs67
AI ReadinessAI-Aware (B)67/100
Agent-ready OpenAPI document · curated by JenticAI/MLMl InferenceapiKey2 EndpointsREST
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For Agents
Score a numeric time series for anomalies in batch or detect whether the most recent point is an outlier given prior history.
Use for: Detect anomalies across the last 30 days of CPU utilisation, Check whether the latest 5-minute revenue point is an outlier, Score an hourly time series of API error counts for anomalies, Find anomalies in a daily order volume series for the last quarter
Not supported: Does not store time series, train custom models, or trigger downstream alerts - use only to score supplied numeric series for anomalies.
Jentic publishes the only available OpenAPI specification for Azure Anomaly Finder Client, keeping it validated and agent-ready. The API runs unsupervised anomaly detection on numeric time series data. Two operations cover the common modes: batch detection over an entire series with a model trained on the supplied points, and last-point detection that scores only the most recent value against a model trained on the prior history. It is a building block for incident detection, fraud signals, and capacity-planning alerts.
Install Jentic One Beta
Connect the Azure Anomaly Finder Client to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Azure Anomaly Finder Client, 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%2Fazure.com%2Fanomaly-finder-client" | 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%2Fazure.com%2Fanomaly-finder-client" | 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 Azure Anomaly Finder Client API.
Detect anomalies across an entire time series in a single batch call
Score only the latest point in a series against a model trained on prior values
Configure sensitivity and granularity (hourly, daily) per request
Return upper, lower, and expected values alongside each anomaly flag
Operate stateless: the model is trained from the points supplied per call
Use Cases
Patterns agents use Azure Anomaly Finder Client API for, with concrete tasks.
★ Operational Metrics Outlier Detection
SRE teams stream KPIs (latency, error rate, throughput) and call /timeseries/last/detect to score the most recent point against the prior window. The API returns isAnomaly, expectedValue, and upper/lower bounds, so an alert pipeline can fire only on statistically significant deviations rather than threshold-based rules.
POST /timeseries/last/detect with the last 168 hourly latency points and granularity=hourly, then alert if isAnomaly is true
Backfill Anomaly Scoring on Historical Series
Analytics teams reviewing a quarter of business KPIs use /timeseries/entire/detect to score the full series in one call. The response flags each point as anomaly or not, supporting offline incident review and cohort analysis without standing up a custom ML pipeline.
POST /timeseries/entire/detect with 90 daily revenue points and granularity=daily and return all indices flagged as anomalies
Sensitivity Tuning for Noisy Signals
Some signals (consumer foot traffic, spiky API calls) are inherently bursty. The sensitivity parameter on both endpoints lets a caller widen or tighten the expected band so that genuine anomalies surface and routine spikes are ignored. This avoids alert fatigue without retraining a custom model.
Run /timeseries/entire/detect with sensitivity=85 and compare flagged points against the default sensitivity=99 result
Agent-Driven Incident Triage
An AI agent reviewing an alert can pull the relevant metric series, call Anomaly Finder, and decide whether the spike is a true outlier before paging a human. Through Jentic, intent search returns the right detect endpoint with its input schema, the API key is held in the credential vault, and the agent gets back a clean isAnomaly verdict in one call.
Search Jentic for 'detect anomaly in time series', load /timeseries/last/detect, and execute against the most recent 24 points with granularity=hourly
Key Endpoints
2 endpoints — jentic publishes the only available openapi specification for azure anomaly finder client, keeping it validated and agent-ready.
METHOD
PATH
DESCRIPTION
POST
/timeseries/entire/detect
Detect anomalies across an entire series
POST
/timeseries/last/detect
Detect whether the latest point is an anomaly
POST
/timeseries/entire/detect
Detect anomalies across an entire series
POST
/timeseries/last/detect
Detect whether the latest point is an anomaly
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.
A
Anomaly Finder Client
- AI-Aware (B)67/100
72
Foundational Compliance63
Developer Experience & Jentic Compatibility55
AI-Readiness & Agent Experience94
Agent Usability50
Security100
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.0
Show dimension breakdown
72
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.
63
Developer Experience & Jentic Compatibility
Clarity, completeness, and ingestion readiness for developers and tooling.
Grade: B-Signals: 4
0%
Example Density
How richly the API is illustrated with examples.
100%
Example Validity
Percentage of examples that conform to their schemas.
50%
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.
55
AI-Readiness & Agent Experience
Semantic breadth, depth, and agent comprehension for AI systems.
Grade: CSignals: 4
71%
Description Coverage
Coverage of descriptions across API elements.
0%
Error Standardization
Coverage of RFC 9457 Problem Details for error responses.
100%
OperationId Quality
Coverage, uniqueness, and casing consistency of operationIds for AI inference.
50%
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.
50
Security
Trust, risk posture, and security compliance.
Grade: C-Signals: 1
50%
Authentication Strength
Average quality of security schemes based on authentication method strength (weakest link for OAuth2).
100
AI Discoverability
Findability, semantic richness, and reasoning readiness.
Grade: A+Signals: 1
100%
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 Azure Anomaly Finder by hand means learning its Cognitive Services subscription-key auth in the Ocp-Apim-Subscription-Key header, picking the right regional endpoint such as westus2.api.cognitive.microsoft.com, and shaping the point series and granularity for each detect call yourself. Through Jentic you install once, import Azure Anomaly Finder from the API Directory, store the subscription key once, and your agent calls it.
Permission scoping
Anomaly Finder carries the time series and its granularity in the request body, not in the URL path, so scoping is by operation rather than by any resource id. Limit the agent to the operations it needs, such as batch detection over an entire series or last-point detection, and it can score numeric series without gaining any operation you did not grant.
Credential isolation
Your Cognitive Services subscription key is stored once, encrypted, by your own Jentic One instance and injected into the Ocp-Apim-Subscription-Key header at execution time. It never enters the agent's prompt, logs, or context.
Intent-based discovery
Agents search Jentic by intent such as 'detect anomaly in time series' or 'check whether the latest metric point is an outlier', and Jentic returns the matching detect operation with its input schema so the agent supplies the points and granularity without reading the Cognitive Services reference.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Azure Anomaly Finder Client API through Jentic.
Why is there no official OpenAPI spec for Azure Anomaly Finder Client?
Microsoft Azure does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Azure Anomaly Finder Client via structured tooling. It is validated against the live API and kept up to date. Get started with Jentic One, the self-hosted execution layer.
What authentication does the Azure Anomaly Finder API use?
The API uses an Azure Cognitive Services subscription key passed in the Ocp-Apim-Subscription-Key header (apiKeyHeader scheme). Through Jentic, the key is stored encrypted in your Jentic One instance and injected at execution time, so agents never see the raw secret.
Can I detect anomalies on streaming metrics with this API?
Yes. Use POST /timeseries/last/detect on each cycle with the recent window of points; the API trains on the supplied prior values and scores the last point. The call is stateless, so you control the window and granularity per request.
What time series granularities are supported?
The endpoints accept the granularity field in the request body. Common values are hourly, daily, and minutely. The granularity must match the spacing of the supplied points; mixing densities produces unstable scores.
What are the rate limits for the Azure Anomaly Finder API?
Limits are tied to the Cognitive Services pricing tier of the resource backing the subscription key. Free F0 tiers cap at low transactions per second; standard tiers go higher. Honour HTTP 429 Retry-After values and batch with /timeseries/entire/detect when scoring long histories.
How do I run anomaly detection on a metric through Jentic?
Run pip install jentic, then search Jentic with 'detect anomaly in time series'. Jentic returns POST /timeseries/last/detect with its input schema. Load the schema, execute with your point series and granularity, and act on isAnomaly. The Cognitive Services key is resolved from the vault automatically.
Can I limit what my agent is allowed to do with the Azure Anomaly Finder API?
Yes. Because you run Jentic One yourself, your own rules decide which operations and credentials the agent may use. This API carries the time series and its granularity in the request body rather than in the URL path, so you scope access by operation: grant only batch detection over an entire series (POST /timeseries/entire/detect), only last-point detection (POST /timeseries/last/detect), or both. The agent can score numeric series with exactly the operations you allow and nothing you did not grant.
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