AI Analysis
The package appears generally benign but has a high metadata risk due to its rapid commit history and low engagement, raising concerns about potential malicious intent.
- High metadata risk
- Rapid commit history with low engagement
Per-check LLM notes
- Network: The presence of an HTTP client suggests the package may make network calls to Azure services for fetching pricing information, which is expected.
- Shell: No shell execution patterns detected, indicating no risk of executing system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The repository's rapid commit history and low engagement suggest potential suspicious activity.
Package Quality Overall: Low (3.4/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Detailed PyPI description (3555 chars)
No contributing guide or governance files found
Development Status classifier >= Beta
Partial type annotation coverage
11 type-annotated function signatures detected in source
Single-author or unverifiable project
1 unique contributor(s) across 11 commits in pimentelleo/azure-pricing-mcpSingle author with few commits — possibly a personal or throwaway project
Heuristic Checks
Found 1 network call pattern(s)
None: self._client = httpx.AsyncClient(timeout=REQUEST_TIMEOUT) async def close(self) -> None:
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
No author email provided
All external links appear legitimate
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forksAll 11 commits happened within 24 hours
1 maintainer concern(s) found
Author "pimentelleo" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Your task is to develop a Python-based mini-application named 'AzureCostEstimator' that leverages the 'azure-pricing-mcp' package to estimate costs for various Azure services based on user input. This application will serve as a valuable tool for both developers and IT administrators who need to forecast costs before deploying resources on Azure. The application should include the following functionalities: 1. User Authentication: Implement a simple authentication mechanism where users must provide their Azure credentials (subscription ID and API key) to access the pricing data. Ensure these credentials are securely handled. 2. Service Selection: Allow users to select from a list of Azure services they wish to estimate costs for, such as Virtual Machines, Storage Accounts, and SQL Databases. 3. Configuration Input: For each selected service, allow users to specify configurations, such as VM sizes, storage types, and database tiers. 4. Cost Estimation: Use the 'azure-pricing-mcp' package to fetch pricing information from the Azure Retail Prices API based on the provided configurations. Display estimated monthly costs for each selected service. 5. Reporting: Provide a summary report at the end of the session, showing total estimated costs across all selected services. 6. User Interface: Design a clean and intuitive command-line interface (CLI) for ease of use. 7. Error Handling: Implement robust error handling to manage cases where the Azure API returns errors or when user inputs are invalid. 8. Documentation: Write clear documentation explaining how to install and run the application, including setup instructions for Azure credentials. This project aims to demonstrate the practical application of the 'azure-pricing-mcp' package in real-world scenarios, providing users with a powerful yet easy-to-use tool for cost management in Azure.
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