mcpbeat Sign in

Azure Monitor Opentelemetry Exporter Java Agent Skill

| Azure Monitor OpenTelemetry Exporter for Java. Export OpenTelemetry traces, metrics, and logs to Azure Monitor/Application Insights.

4k tokens
context cost
the whole folder, loaded on every use
2
files
instructions only
0
copies elsewhere
how many repositories repackaged it
152 d ago
last touched
this folder, not the whole repository

Install

one command, takes just this skill from the repository
npx skills add https://github.com/microsoft/skills --skill azure-monitor-opentelemetry-exporter-java

What comes with it

5 513 bytes besides the instruction
references/examples.md

The instruction itself

18 sections, as written by the author

Azure Monitor OpenTelemetry Exporter for Java

> ⚠️ DEPRECATION NOTICE: This package is deprecated. Migrate to azure-monitor-opentelemetry-autoconfigure.

>

> See Migration Guide for detailed instructions.

Export OpenTelemetry telemetry data to Azure Monitor / Application Insights.

Installation (Deprecated)

<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-monitor-opentelemetry-exporter</artifactId>
    <version>1.0.0-beta.x</version>
</dependency>
<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-monitor-opentelemetry-autoconfigure</artifactId>
    <version>LATEST</version>
</dependency>

Environment Variables

APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.com/

Using Environment Variable

import io.opentelemetry.sdk.autoconfigure.AutoConfiguredOpenTelemetrySdk;
import io.opentelemetry.sdk.autoconfigure.AutoConfiguredOpenTelemetrySdkBuilder;
import io.opentelemetry.api.OpenTelemetry;
import com.azure.monitor.opentelemetry.exporter.AzureMonitorExporter;

// Connection string from APPLICATIONINSIGHTS_CONNECTION_STRING env var
AutoConfiguredOpenTelemetrySdkBuilder sdkBuilder = AutoConfiguredOpenTelemetrySdk.builder();
AzureMonitorExporter.customize(sdkBuilder);
OpenTelemetry openTelemetry = sdkBuilder.build().getOpenTelemetrySdk();

With Explicit Connection String

AutoConfiguredOpenTelemetrySdkBuilder sdkBuilder = AutoConfiguredOpenTelemetrySdk.builder();
AzureMonitorExporter.customize(sdkBuilder, "{connection-string}");
OpenTelemetry openTelemetry = sdkBuilder.build().getOpenTelemetrySdk();

Creating Spans

import io.opentelemetry.api.trace.Tracer;
import io.opentelemetry.api.trace.Span;
import io.opentelemetry.context.Scope;

// Get tracer
Tracer tracer = openTelemetry.getTracer("com.example.myapp");

// Create span
Span span = tracer.spanBuilder("myOperation").startSpan();

try (Scope scope = span.makeCurrent()) {
    // Your application logic
    doWork();
} catch (Throwable t) {
    span.recordException(t);
    throw t;
} finally {
    span.end();
}

Adding Span Attributes

import io.opentelemetry.api.common.AttributeKey;
import io.opentelemetry.api.common.Attributes;

Span span = tracer.spanBuilder("processOrder")
    .setAttribute("order.id", "12345")
    .setAttribute("customer.tier", "premium")
    .startSpan();

try (Scope scope = span.makeCurrent()) {
    // Add attributes during execution
    span.setAttribute("items.count", 3);
    span.setAttribute("total.amount", 99.99);
    
    processOrder();
} finally {
    span.end();
}

Custom Span Processor

import io.opentelemetry.sdk.trace.SpanProcessor;
import io.opentelemetry.sdk.trace.ReadWriteSpan;
import io.opentelemetry.sdk.trace.ReadableSpan;
import io.opentelemetry.context.Context;

private static final AttributeKey<String> CUSTOM_ATTR = AttributeKey.stringKey("custom.attribute");

SpanProcessor customProcessor = new SpanProcessor() {
    @Override
    public void onStart(Context context, ReadWriteSpan span) {
        // Add custom attribute to every span
        span.setAttribute(CUSTOM_ATTR, "customValue");
    }

    @Override
    public boolean isStartRequired() {
        return true;
    }

    @Override
    public void onEnd(ReadableSpan span) {
        // Post-processing if needed
    }

    @Override
    public boolean isEndRequired() {
        return false;
    }
};

// Register processor
AutoConfiguredOpenTelemetrySdkBuilder sdkBuilder = AutoConfiguredOpenTelemetrySdk.builder();
AzureMonitorExporter.customize(sdkBuilder);

sdkBuilder.addTracerProviderCustomizer(
    (sdkTracerProviderBuilder, configProperties) -> 
        sdkTracerProviderBuilder.addSpanProcessor(customProcessor)
);

OpenTelemetry openTelemetry = sdkBuilder.build().getOpenTelemetrySdk();

Nested Spans

public void parentOperation() {
    Span parentSpan = tracer.spanBuilder("parentOperation").startSpan();
    try (Scope scope = parentSpan.makeCurrent()) {
        childOperation();
    } finally {
        parentSpan.end();
    }
}

public void childOperation() {
    // Automatically links to parent via Context
    Span childSpan = tracer.spanBuilder("childOperation").startSpan();
    try (Scope scope = childSpan.makeCurrent()) {
        // Child work
    } finally {
        childSpan.end();
    }
}

Recording Exceptions

Span span = tracer.spanBuilder("riskyOperation").startSpan();
try (Scope scope = span.makeCurrent()) {
    performRiskyWork();
} catch (Exception e) {
    span.recordException(e);
    span.setStatus(StatusCode.ERROR, e.getMessage());
    throw e;
} finally {
    span.end();
}

Metrics (via OpenTelemetry)

import io.opentelemetry.api.metrics.Meter;
import io.opentelemetry.api.metrics.LongCounter;
import io.opentelemetry.api.metrics.LongHistogram;

Meter meter = openTelemetry.getMeter("com.example.myapp");

// Counter
LongCounter requestCounter = meter.counterBuilder("http.requests")
    .setDescription("Total HTTP requests")
    .setUnit("requests")
    .build();

requestCounter.add(1, Attributes.of(
    AttributeKey.stringKey("http.method"), "GET",
    AttributeKey.longKey("http.status_code"), 200L
));

// Histogram
LongHistogram latencyHistogram = meter.histogramBuilder("http.latency")
    .setDescription("Request latency")
    .setUnit("ms")
    .ofLongs()
    .build();

latencyHistogram.record(150, Attributes.of(
    AttributeKey.stringKey("http.route"), "/api/users"
));

Key Concepts

| Concept | Description |

|---------|-------------|

| Connection String | Application Insights connection string with instrumentation key |

| Tracer | Creates spans for distributed tracing |

| Span | Represents a unit of work with timing and attributes |

| SpanProcessor | Intercepts span lifecycle for customization |

| Exporter | Sends telemetry to Azure Monitor |

Migration to Autoconfigure

The azure-monitor-opentelemetry-autoconfigure package provides:

  • Automatic instrumentation of common libraries
  • Simplified configuration
  • Better integration with OpenTelemetry SDK

Migration Steps

  • Replace dependency:
   <!-- Remove -->
   <dependency>
       <groupId>com.azure</groupId>
       <artifactId>azure-monitor-opentelemetry-exporter</artifactId>
   </dependency>
   
   <!-- Add -->
   <dependency>
       <groupId>com.azure</groupId>
       <artifactId>azure-monitor-opentelemetry-autoconfigure</artifactId>
   </dependency>

Best Practices

  • Use autoconfigure — Migrate to azure-monitor-opentelemetry-autoconfigure
  • Set meaningful span names — Use descriptive operation names
  • Add relevant attributes — Include contextual data for debugging
  • Handle exceptions — Always record exceptions on spans
  • Use semantic conventions — Follow OpenTelemetry semantic conventions
  • End spans in finally — Ensure spans are always ended
  • Use try-with-resources — Scope management with try-with-resources pattern

| Resource | URL |

|----------|-----|

| Maven Package | https://central.sonatype.com/artifact/com.azure/azure-monitor-opentelemetry-exporter |

| GitHub | https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/monitor/azure-monitor-opentelemetry-exporter |

| Migration Guide | https://github.com/Azure/azure-sdk-for-java/blob/main/sdk/monitor/azure-monitor-opentelemetry-exporter/MIGRATION.md |

| Autoconfigure Package | https://central.sonatype.com/artifact/com.azure/azure-monitor-opentelemetry-autoconfigure |

| OpenTelemetry Java | https://opentelemetry.io/docs/languages/java/ |

| Application Insights | https://learn.microsoft.com/azure/azure-monitor/app/app-insights-overview |

Other skills for the same job

different authors, same section of the catalogue
Lamindb
by christophacham
×3

This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

22k tokens
Latchbio Integration
by christophacham
×3

Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

12k tokens
Modal
by christophacham
×3

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.

17k tokens
Pyhealth
by christophacham
×3

Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).

22k tokens
Github Workflow Automation
by ComeOnOliver
×3

Advanced GitHub Actions workflow automation with AI swarm coordination, intelligent CI/CD pipelines, and comprehensive repository management

9k tokens
Lamindb
by ComeOnOliver
×3

This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

41k tokens
Ml Pipeline Workflow
by ComeOnOliver
×3

Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.

5k tokens
Pyhealth
by ComeOnOliver
×3

Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).

39k tokens

How to use it

Copy the folder

Take microsoft/azure-monitor-opentelemetry-exporter-java from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.