mcpbeat Sign in

Azure Monitor Opentelemetry Py Agent Skill

| Azure Monitor OpenTelemetry Distro for Python. Use for one-line Application Insights setup with auto-instrumentation.

4k tokens
context cost
the whole folder, loaded on every use
3
files
instructions only
0
copies elsewhere
how many repositories repackaged it
52 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-py

What comes with it

6 225 bytes besides the instruction
references/capabilities.md
references/non-hero-scenarios.md

The instruction itself

21 sections, as written by the author

Azure Monitor OpenTelemetry Distro for Python

One-line setup for Application Insights with OpenTelemetry auto-instrumentation.

Installation

pip install azure-monitor-opentelemetry

Environment Variables

APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.com/  # Required for all auth methods
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production

Authentication & Lifecycle

> 🔑 Two rules apply to every code sample below:

>

> 1. Prefer DefaultAzureCredential for ingestion auth when supported. APPLICATIONINSIGHTS_CONNECTION_STRING identifies the target Application Insights resource, and credential=DefaultAzureCredential(...) provides Microsoft Entra authentication.

> - Local dev: DefaultAzureCredential works as-is.

> - Production: set AZURE_TOKEN_CREDENTIALS=prod (or AZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.

> 2. Providers are not context managers. Flush and shut down telemetry providers explicitly at process exit so buffers are exported deterministically.

>

> Snippets may abbreviate this setup, but production code should always follow both rules.

Quick Start

from azure.identity import DefaultAzureCredential
from azure.monitor.opentelemetry import configure_azure_monitor

# Connection string identifies the App Insights resource (read from APPLICATIONINSIGHTS_CONNECTION_STRING env var).
# DefaultAzureCredential authenticates ingestion via Microsoft Entra ID (preferred over instrumentation-key-only auth).
configure_azure_monitor(
    credential=DefaultAzureCredential(),
)

# Your application code...

Explicit Connection String

Pass the connection string explicitly by reading it from the environment variable.

The value includes both InstrumentationKey and IngestionEndpoint.

import os
from azure.monitor.opentelemetry import configure_azure_monitor

# Read the full connection string from the environment.
# Format: "InstrumentationKey=<key>;IngestionEndpoint=https://<id>.in.applicationinsights.azure.com/"
connection_string = os.environ["APPLICATIONINSIGHTS_CONNECTION_STRING"]

try:
    configure_azure_monitor(
        connection_string=connection_string,
    )
    # Your application code...
except Exception as exc:
    raise RuntimeError(f"Azure Monitor configuration failed: {exc}") from exc

With Flask

from flask import Flask
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

app = Flask(__name__)

@app.route("/")
def hello():
    return "Hello, World!"

if __name__ == "__main__":
    app.run()

With Django

# settings.py
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

# Django settings...

With FastAPI

from fastapi import FastAPI
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

app = FastAPI()

@app.get("/")
async def root():
    return {"message": "Hello World"}

Custom Traces

from opentelemetry import trace
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

tracer = trace.get_tracer(__name__)

with tracer.start_as_current_span("my-operation") as span:
    span.set_attribute("custom.attribute", "value")
    # Do work...

Custom Metrics

from opentelemetry import metrics
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

meter = metrics.get_meter(__name__)
counter = meter.create_counter("my_counter")

counter.add(1, {"dimension": "value"})

Custom Logs

import logging
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)

logger.info("This will appear in Application Insights")
logger.error("Errors are captured too", exc_info=True)

Sampling

from azure.monitor.opentelemetry import configure_azure_monitor

# Sample 10% of requests
configure_azure_monitor(
    sampling_ratio=0.1
)

Cloud Role Name

Set cloud role name for Application Map:

from azure.monitor.opentelemetry import configure_azure_monitor
from opentelemetry.sdk.resources import Resource, SERVICE_NAME

configure_azure_monitor(
    resource=Resource.create({SERVICE_NAME: "my-service-name"})
)

Disable Specific Instrumentations

from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor(
    instrumentations=["flask", "requests"]  # Only enable these
)

Enable Live Metrics

from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor(
    enable_live_metrics=True
)

Azure AD Authentication

from azure.monitor.opentelemetry import configure_azure_monitor
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential

# Local dev: DefaultAzureCredential. In production, set AZURE_TOKEN_CREDENTIALS=prod or use a specific credential.
credential = DefaultAzureCredential()
# Or use a specific credential directly in production:
# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()

configure_azure_monitor(
    credential=credential
)

Auto-Instrumentations Included

| Library | Telemetry Type |

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

| Flask | Traces |

| Django | Traces |

| FastAPI | Traces |

| Requests | Traces |

| urllib3 | Traces |

| httpx | Traces |

| aiohttp | Traces |

| psycopg2 | Traces |

| pymysql | Traces |

| pymongo | Traces |

| redis | Traces |

Configuration Options

| Parameter | Description | Default |

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

| connection_string | Application Insights connection string | From env var |

| credential | Azure credential for AAD auth | None |

| sampling_ratio | Sampling rate (0.0 to 1.0) | 1.0 |

| resource | OpenTelemetry Resource | Auto-detected |

| instrumentations | List of instrumentations to enable | All |

| enable_live_metrics | Enable Live Metrics stream | False |

Best Practices

  • Pick sync OR async and stay consistent. Do not mix azure.xxx sync clients with azure.xxx.aio async clients in the same call path. Choose one mode per module.
  • Call provider.shutdown() / force_flush() at process exit to flush telemetry — providers are not context managers.
  • Call configure_azure_monitor() early — Before importing instrumented libraries
  • Use environment variables for connection string in production
  • Set cloud role name for multi-service applications
  • Enable sampling in high-traffic applications
  • Use structured logging for better log analytics queries
  • Add custom attributes to spans for better debugging
  • Use Microsoft Entra authentication for production workloads

Reference Files

| File | Contents |

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

| references/capabilities.md | Additional non-hero capabilities, operation-group coverage, and production checklists. |

| references/non-hero-scenarios.md | Dedicated non-hero examples for secondary/advanced scenarios. |

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-py 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.

Install what it needs

The instructions reference pip. Without those the skill loads but fails at the first command.