AI Analysis
The package has minimal risks associated with network and shell operations. The metadata suggests a new or less active account, but this alone does not confirm any malicious intent.
- Low shell risk
- Moderate network risk
- Single package from author
Per-check LLM notes
- Network: The use of network calls to external URLs is common in packages that require fetching remote resources or reporting test results.
- Shell: No shell execution patterns detected, indicating low risk for executing arbitrary commands.
- Metadata: The author has only one package on PyPI, which may indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.
Package Quality Overall: Medium (6.2/10)
Test suite present — 11 test file(s) found
Test runner config found: pyproject.toml11 test file(s) detected (e.g. test_client.py)
Some documentation present
Detailed PyPI description (11993 chars)
No contributing guide or governance files found
Development Status classifier >= Beta
Partial type annotation coverage
44 type-annotated function signatures detected in source
Active multi-contributor project
4 unique contributor(s) across 45 commits in mshegolev/allure-testops-mcpSmall but multi-author team (3–4 contributors)
Heuristic Checks
Found 2 network call pattern(s)
pi/rs" self.session = requests.Session() self.session.headers.update( {ised.""" try: r = requests.Session().get(url, timeout=5) r.raise_for_status() excep
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
Repository mshegolev/allure-testops-mcp appears legitimate
1 maintainer concern(s) found
Author "Mikhail Shchegolev" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a comprehensive testing dashboard application using Python and the 'allure-testops-mcp' package. This application will serve as a bridge between your local testing environment and Allure TestOps, enabling real-time tracking of test cases, launches, and results. Here’s a detailed breakdown of the steps and features you'll need to implement:
1. **Setup Project**: Initialize a new Python project and install the necessary packages including 'allure-testops-mcp'. Ensure your environment is configured correctly to interact with Allure TestOps.
2. **Authentication Module**: Implement an authentication system that securely connects to your Allure TestOps instance. Use tokens or API keys for authentication.
3. **Test Case Management**: Develop functionality to create, update, and delete test cases directly from your application. Utilize the 'allure-testops-mcp' package to sync these changes with Allure TestOps.
4. **Launch Management**: Allow users to start, stop, and manage test runs ('launches') through the application interface. Integrate the ability to attach test results to specific launches.
5. **Real-Time Reporting**: Build a feature that fetches and displays real-time status updates on test cases and launches. This could include pass/fail rates, progress bars, and more.
6. **User Interface**: Design a clean, user-friendly interface using a web framework like Flask or Django. The UI should allow users to easily navigate through their tests and view reports.
7. **Customization Options**: Provide options for users to customize their test cases and launches according to their needs. This includes adding descriptions, tags, and custom parameters.
8. **Integration Testing**: Before deploying, ensure thorough integration testing is conducted to verify all components work seamlessly together.
9. **Deployment Guide**: Prepare a guide detailing how to deploy your application in different environments (local, cloud, etc.).
By following these steps and utilizing the 'allure-testops-mcp' package effectively, you will have built a powerful tool that enhances the efficiency and effectiveness of your testing processes.
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