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Azure API Center Management SDK for Python

azure-mgmt-apicenter-py

Azure API Center Management SDK for Python. Use for managing API inventory, metadata, and governance across your organization.

techwavedev/agi-agent-kit0installs4stars

SKILL.md

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Azure API Center Management SDK for Python

Manage API inventory, metadata, and governance in Azure API Center.

Installation

pip install azure-mgmt-apicenter
pip install azure-identity

Environment Variables

AZURE_SUBSCRIPTION_ID=your-subscription-id

Authentication

from azure.identity import DefaultAzureCredential
from azure.mgmt.apicenter import ApiCenterMgmtClient
import os

client = ApiCenterMgmtClient(
    credential=DefaultAzureCredential(),
    subscription_id=os.environ["AZURE_SUBSCRIPTION_ID"]
)

Create API Center

from azure.mgmt.apicenter.models import Service

api_center = client.services.create_or_update(
    resource_group_name="my-resource-group",
    service_name="my-api-center",
    resource=Service(
        location="eastus",
        tags={"environment": "production"}
    )
)

print(f"Created API Center: {api_center.name}")

List API Centers

api_centers = client.services.list_by_subscription()

for api_center in api_centers:
    print(f"{api_center.name} - {api_center.location}")

Register an API

from azure.mgmt.apicenter.models import Api, ApiKind, LifecycleStage

api = client.apis.create_or_update(
    resource_group_name="my-resource-group",
    service_name="my-api-center",
    workspace_name="default",
    api_name="my-api",
    resource=Api(
        title="My API",
        description="A sample API for demonstration",
        kind=ApiKind.REST,
        lifecycle_stage=LifecycleStage.PRODUCTION,
        terms_of_service={"url": "https://example.com/​terms"},
        contacts=[{"name": "API Team", "email": "[email protected]"}]
    )
)

print(f"Registered API: {api.title}")

Create API Version

from azure.mgmt.apicenter.models import ApiVersion, LifecycleStage

version = client.api_versions.create_or_update(
    resource_group_name="my-resource-group",
    service_name="my-api-center",
    workspace_name="default",
    api_name="my-api",
    version_name="v1",
    resource=ApiVersion(
        title="Version 1.0",
        lifecycle_stage=LifecycleStage.PRODUCTION
    )
)

print(f"Created version: {version.title}")

Add API Definition

from azure.mgmt.apicenter.models import ApiDefinition

definition = client.api_definitions.create_or_update(
    resource_group_name="my-resource-group",
    service_name="my-api-center",
    workspace_name="default",
    api_name="my-api",
    version_name="v1",
    definition_name="openapi",
    resource=ApiDefinition(
        title="OpenAPI Definition",
        description="OpenAPI 3.0 specification"
    )
)

Import API Specification

from azure.mgmt.apicenter.models import ApiSpecImportRequest, ApiSpecImportSourceFormat

# Import from inline content
client.api_definitions.import_specification(
    resource_group_name="my-resource-group",
    service_name="my-api-center",
    workspace_name="default",
    api_name="my-api",
    version_name="v1",
    definition_name="openapi",
    body=ApiSpecImportRequest(
        format=ApiSpecImportSourceFormat.INLINE,
        value='{"openapi": "3.0.0", "info": {"title": "My API", "version": "1.0"}, "paths": {}}'
    )
)

List APIs

apis = client.apis.list(
    resource_group_name="my-resource-group",
    service_name="my-api-center",
    workspace_name="default"
)

for api in apis:
    print(f"{api.name}: {api.title} ({api.kind})")

Create Environment

from azure.mgmt.apicenter.models import Environment, EnvironmentKind

environment = client.environments.create_or_update(
    resource_group_name="my-resource-group",
    service_name="my-api-center",
    workspace_name="default",
    environment_name="production",
    resource=Environment(
        title="Production",
        description="Production environment",
        kind=EnvironmentKind.PRODUCTION,
        server={"type": "Azure API Management", "management_portal_uri": ["https://portal.azure.com"]}
    )
)

Create Deployment

from azure.mgmt.apicenter.models import Deployment, DeploymentState

deployment = client.deployments.create_or_update(
    resource_group_name="my-resource-group",
    service_name="my-api-center",
    workspace_name="default",
    api_name="my-api",
    deployment_name="prod-deployment",
    resource=Deployment(
        title="Production Deployment",
        description="Deployed to production APIM",
        environment_id="/​workspaces/​default/​environments/​production",
        definition_id="/​workspaces/​default/​apis/​my-api/​versions/​v1/​definitions/​openapi",
        state=DeploymentState.ACTIVE,
        server={"runtime_uri": ["https://api.example.com"]}
    )
)

Define Custom Metadata

from azure.mgmt.apicenter.models import MetadataSchema

metadata = client.metadata_schemas.create_or_update(
    resource_group_name="my-resource-group",
    service_name="my-api-center",
    metadata_schema_name="data-classification",
    resource=MetadataSchema(
        schema='{"type": "string", "title": "Data Classification", "enum": ["public", "internal", "confidential"]}'
    )
)

Client Types

ClientPurpose
ApiCenterMgmtClientMain client for all operations

Operations

Operation GroupPurpose
servicesAPI Center service management
workspacesWorkspace management
apisAPI registration and management
api_versionsAPI version management
api_definitionsAPI definition management
deploymentsDeployment tracking
environmentsEnvironment management
metadata_schemasCustom metadata definitions

Best Practices

  1. Use workspaces to organize APIs by team or domain
  2. Define metadata schemas for consistent governance
  3. Track deployments to understand where APIs are running
  4. Import specifications to enable API analysis and linting
  5. Use lifecycle stages to track API maturity
  6. Add contacts for API ownership and support

When to Use

This skill is applicable to execute the workflow or actions described in the overview.


<!-- AGI-INTEGRATION-START -->

AGI Framework Integration

Adapted for @techwavedev/​agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior API design decisions, database schema choices, and error handling patterns. Cache API response templates for consistent error formatting.

# Check for prior backend/​API context before starting
python3 execution/​memory_manager.py auto --query "API design patterns and architecture decisions for Azure Mgmt Apicenter Py"

Storing Results

After completing work, store backend/​API decisions for future sessions:

python3 execution/​memory_manager.py store \
  --content "API architecture: REST with HATEOAS, JWT auth, rate limiting at 100 req/​min per tenant" \
  --type decision --project <project> \
  --tags azure-mgmt-apicenter-py backend

Multi-Agent Collaboration

Share API contract changes with frontend agents so they update their client code, and with QA agents for test coverage.

python3 execution/​cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Implemented API endpoints — 5 new routes with OpenAPI spec and integration tests" \
  --project <project>

Agent Team: Code Review

After implementation, dispatch code_review_team for two-stage review (spec compliance + code quality) before merging.

<!-- AGI-INTEGRATION-END -->

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