Azure Functions Doctor

Part of the Azure Functions Python DX Toolkit — dogfood-tested by azure-functions-cookbook-python.

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Azure Functions Doctor is the pre-deploy health gate for Azure Functions Python v2 projects — a diagnostic CLI that catches configuration issues, missing dependencies, and environment problems before they cause runtime failures in production.

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Part of the Azure Functions Python DX Toolkit → Bring FastAPI-like developer experience to Azure Functions

Why this exists

Deploying a broken Azure Functions app is expensive — the worker starts, the host reads config, and only then does it surface the issue in a production log. Common problems that slip through:

• Missing dependencies — azure-functions not in requirements.txt, discovered only at cold start
• Invalid configuration — host.json misconfigured, extensionBundle missing or outdated
• Runtime incompatibilities — Python version mismatch with Azure Functions runtime
• Silent failures — no virtual environment, Core Tools not installed, Application Insights key missing

azure-functions-doctor moves that failure left — catch it locally or in CI, not in production.

What it does

• 14+ diagnostic checks — Python version, dependencies, host.json, Core Tools, Durable Functions, and more
• Multiple output formats — table, JSON, SARIF, JUnit for CI integration
• Profile support — minimal or full rulesets depending on your needs
• Official GitHub Action — yeongseon/azure-functions-doctor@v1 for CI gates

Scope

This repository targets the decorator-based Azure Functions Python v2 programming model only. Non-v2 repositories are detected up front and reported as unsupported instead of running v2-only checks.

• Supported model: func.FunctionApp() with decorators such as @app.route()
• Unsupported model: legacy function.json-based Python v1 projects

Use azure-functions-doctor as part of a pre-deployment checklist alongside azure-functions-logging for observability.

What this package does not do

This package does not own:

• Fixing issues — it diagnoses configuration problems but does not auto-fix them
• API documentation — use azure-functions-openapi for API documentation and spec generation
• Request validation — use azure-functions-validation for request/response validation and serialization

Installation

From PyPI:

pip install azure-functions-doctor

From source:

git clone https://github.com/yeongseon/azure-functions-doctor-python.git
cd azure-functions-doctor-python
python3 -m venv .venv
source .venv/bin/activate
pip install -e .

Quick Start

Run the doctor in the current project:

azure-functions doctor

Run against a specific project:

azure-functions doctor --path ./examples/v2/http-trigger

Use a required-only profile:

azure-functions doctor --profile minimal

Output JSON for CI:

azure-functions doctor --format json

Pin the Azure Functions target runtime explicitly:

azure-functions doctor --target-python 3.12

Use --target-python when the Python running azure-functions-doctor is not the same as the Python version your Function App will run on Azure.

Sample output (excerpt)

azure-functions doctor --path ./examples/v2/http-trigger

Azure Functions Doctor
Path: ./examples/v2/http-trigger

Programming Model
[✓] Programming model v2: Keyword '@app.|@bp.' found in source code (AST)

Python Env
[✓] Python version: Python 3.10.12 (tool runtime, >=3.10)
[✓] requirements.txt: requirements.txt exists
[✓] azure-functions package: Package 'azure-functions' declared in requirements.txt

Project Structure
[✓] host.json: host.json exists
[✓] host.json version: host.json version is "2.0"

Tooling
[✓] Azure Functions Core Tools (func): func detected

...

Doctor summary:
  0 fails, 5 warnings, 15 passed
Exit code: 0

The same command runs in CI pipelines — see CI Integration below and docs/deployment.md for details.

CI Integration

Use azure-functions-doctor as a CI gate to block deployments on required failures.

GitHub Actions (CLI)

- name: Run azure-functions-doctor
  run: |
    pip install azure-functions-doctor
    azure-functions doctor --profile minimal --format json --output doctor.json

- name: Upload report
  if: always()
  uses: actions/upload-artifact@v4
  with:
    name: doctor-report
    path: doctor.json

Official GitHub Action

- uses: yeongseon/azure-functions-doctor@v1
  with:
    path: .
    profile: minimal
    format: sarif
    output: doctor.sarif
    upload-sarif: "true"

See docs/examples/ci_integration.md for Azure DevOps, pre-commit, VS Code, and SARIF upload examples.

Demo

The demo below is generated from demo/doctor-demo.tape with VHS. It runs the real azure-functions doctor CLI against the representative example and then against an intentionally broken copy to show the pass/fail contrast.

The final terminal state is also captured as a static image for quick inspection.

Default ruleset

The default ruleset includes checks for:

• Azure Functions Python v2 decorator usage
• Python version
• virtual environment activation
• Python executable availability
• requirements.txt
• azure-functions dependency declaration
• host.json
• local.settings.json (optional)
• Azure Functions Core Tools presence and version (optional)
• Durable Functions host configuration (optional)
• Application Insights configuration (optional)
• extensionBundle configuration (optional)
• ASGI/WSGI callable exposure (optional)
• common unwanted files in the project tree (optional)

Examples

• examples/v2/http-trigger/README.md
• examples/v2/multi-trigger/README.md

Requirements

• Python 3.10+
• Hatch for development workflows
• Azure Functions Core Tools v4+ recommended for local runs

When to use

• Before deploying an Azure Functions app (local pre-flight check)
• In CI/CD pipelines as a deployment gate
• When onboarding a new developer to catch environment setup issues
• After upgrading Python version or Azure Functions runtime
• As a pre-commit hook for configuration validation

How It Works

azure-functions doctor loads a JSON ruleset, dispatches each rule to a type-based handler, and aggregates the results into per-section output:

flowchart LR
    CLI["cli.py<br/>Typer CLI"] --> DOC["doctor.py<br/>Diagnostic runner"]
    DOC --> RULES[("assets/<br/>Rule inventory")]
    DOC --> HDLR["handlers.py<br/>Type-based dispatch"]
    HDLR --> TR["target_resolver.py<br/>Version resolution"]
    DOC --> RES["SectionResult<br/>+ CheckResult"]
    RES --> OUT["table / json<br/>sarif / junit"]

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See docs/architecture.md for the full component and sequence diagrams, and docs/diagnostics.md for the rule-evaluation pipeline.

Documentation

• docs/index.md
• docs/usage.md
• docs/rules.md
• docs/diagnostics.md
• docs/development.md
• docs/examples/ci_integration.md

Ecosystem

This package is part of the Azure Functions Python DX Toolkit.

Design principle: azure-functions-doctor owns pre-deploy diagnostics. It does not fix issues or generate code — it surfaces actionable findings so developers can fix them. Runtime behavior belongs to azure-functions-openapi (API documentation and spec generation), azure-functions-validation (request/response validation), and azure-functions-langgraph (LangGraph runtime exposure).

Package	Role
azure-functions-openapi-python	OpenAPI spec generation and Swagger UI
azure-functions-validation-python	Request/response validation and serialization
azure-functions-db-python	SQLAlchemy-powered DB integration helpers (poll-based pseudo trigger, input/output/client injection)
azure-functions-langgraph-python	LangGraph deployment adapter for Azure Functions
azure-functions-scaffold-python	Project scaffolding CLI
azure-functions-logging-python	Structured logging and observability
azure-functions-doctor-python	Pre-deploy diagnostic CLI
azure-functions-durable-graph-python	Manifest-first graph runtime with Durable Functions (experimental)
azure-functions-knowledge-python	Knowledge retrieval (RAG) decorators
azure-functions-cookbook-python	Dogfood examples — runnable recipes that exercise the full toolkit

For AI Coding Assistants

This repository includes llms.txt and llms-full.txt for LLM-friendly documentation:

• llms.txt — Concise index of package info, CLI commands, quick start, and ecosystem overview
• llms-full.txt — Comprehensive API reference with output formats, diagnostic rules, custom rules, and CI integration patterns

When working with this codebase, LLM assistants should:

1. Use llms.txt for quick reference — package version, Python requirements (>=3.10,<3.15), CLI entry points
2. Refer to llms-full.txt for implementation details — output contracts, rule structure, custom rule patterns, handler types
3. Check src/azure_functions_doctor/cli.py — authoritative source for CLI options and validation
4. Review src/azure_functions_doctor/assets/rules/v2.json — complete ruleset with check definitions
5. Consult src/azure_functions_doctor/handlers.py — diagnostic rule handlers and pattern matchers

For bug reports, feature requests, or documentation improvements, please open an issue or pull request on GitHub.

Disclaimer

This project is an independent community project and is not affiliated with, endorsed by, or maintained by Microsoft.

Azure and Azure Functions are trademarks of Microsoft Corporation.

License

MIT