Azpolicymcp

🚀 AzPolicy: An MCP-based Policy Management Tool

This project aims to develop a policy management tool (AzPolicy) based on the MCP protocol, focusing on the definition, validation, and deployment of Azure policies (AzPolicy). The tool interacts with Azure services through REST APIs, uses JSON Schema for data validation, and leverages the GitHub API to obtain policy examples. To enhance maintainability and scalability, the project adopts a modular design and provides detailed documentation and test cases.

✨ Features

AzPolicy Definition

• Support downloading Azure policy definitions from GitHub repositories.
• Provide JSON Schema validation to ensure correct policy formats.
• Allow users to customize and extend policy definitions.

AzPolicy Validation Tool

• Use the jsonschema library to strictly validate policy files.
• Support multiple validation modes, including patterns, delimiters, and exclusion rules.
• Provide detailed error reports for quick issue identification.

AzPolicy Deployment

• Implement policy publishing and management through the Azure REST API.
• Support policy synchronization in multi-tenant environments.
• Provide parameterized configurations to adapt to different business scenarios.

🔧 Technical Details

MCP Protocol

Based on the Model Context Protocol (MCP), it is used to implement model context exchange, ensuring data consistency and interoperability between systems.

REST API Integration

Use the requests library to interact with Azure services, supporting authentication, error handling, and logging functions.

JSON Schema Validation

Use the jsonschema library to perform structured validation on policy files, ensuring they meet predefined schema requirements.

GitHub Interface

Integrate the GitHub API to obtain the latest policy examples and documentation information, supporting version control and collaborative development.

💻 Usage Examples

Basic Usage

import requests
from jsonschema import validate

def fetch_policy_from_github(repo, path):
    url = f"https://api.github.com/repos/{repo}/contents/{path}"
    response = requests.get(url)
    if response.status_code == 200:
        return response.json()
    else:
        raise Exception(f"Failed to fetch policy: {response.text}")

def validate_policy(policy_json, schema):
    try:
        validate(instance=policy_json, schema=schema)
        return True
    except jsonschema.ValidationError as e:
        print(f"Validation error: {str(e)}")
        return False

Advanced Usage

import os
from azure.common.credentials import ServicePrincipalCredentials

def deploy_policy(subscription_id, resource_group_name, policy_set_name, policy_file):
    credentials = ServicePrincipalCredentials(
        client_id=os.getenv('AZURE_CLIENT_ID'),
        secret=os.getenv('AZURE_CLIENT_SECRET'),
        tenant=os.getenv('AZURE_TENANT_ID')
    )
    # Implement Azure Policy deployment logic here

📚 Documentation

Risk Analysis and Mitigation Strategies

Risk of Policy Validation Failure

• Cause: The input policy file does not conform to the predefined JSON Schema.
• Mitigation: Add detailed error logs and user-friendly prompt messages to guide users in fixing issues.

GitHub API Access Restrictions

• Cause: Frequent calls to the GitHub API may result in IP bans or rate limits.
• Mitigation: Introduce request intervals, use proxy pools, and cache retrieved policy examples to reduce redundant requests.

Azure Service Unavailability

• Cause: Failures in Azure cloud services or network issues may lead to API call failures.
• Mitigation: Add retry mechanisms and error handling logic to ensure the system has good fault tolerance.

Project Value

• Improve Policy Management Efficiency: Automate policy validation and deployment to reduce human errors.
• Enhance System Stability: Use strict format checks and comprehensive test cases to ensure system robustness and reliability.
• Facilitate Team Collaboration: Provide unified interfaces and documentation for developers to quickly get started and conduct efficient code reviews.

Future Expansion

• Support Multi-cloud Environments: Gradually expand support for other cloud platforms such as AWS and Google Cloud based on the existing Azure support.
• Introduce AI-assisted Validation: Use machine learning techniques to predict potential policy conflicts and provide optimization suggestions.
• Build a Visual Interface: Develop a graphical user interface for non-technical users to view and manage complex policy configurations.