🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to potential issues with metadata quality and low maintainer activity, despite having low risks in network, shell, obfuscation, and credential aspects.

Metadata risk of 5/10 due to poor metadata quality and low maintainer activity
Installation method is unconventional and might be misleading

Per-check LLM notes

Network: Network calls are expected for packages that need to interact with external services or APIs.
Shell: No shell execution patterns detected, indicating no immediate risk from command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, raising suspicion but not definitive evidence of malice.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 20 test file(s) found

Test runner config found: conftest.py
Test runner config found: pyproject.toml
20 test file(s) detected (e.g. test_case.py)

◈ Medium Documentation 5.0

Some documentation present

Brief PyPI description (741 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

332 type-annotated function signatures detected in source

○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

ient") self._client = httpx.AsyncClient( base_url=self._base_url, headers={

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with aio-tests-mcp

Create a Python-based utility called 'JiraTestRunner' that leverages the 'aio-tests-mcp' package to manage and execute automated tests for Jira Cloud projects. This tool will serve as a bridge between local test environments and Jira Cloud, allowing developers to seamlessly integrate their testing workflows into Jira's project management system. Here's a detailed breakdown of the project's requirements and functionalities:

1. **Setup and Configuration**: Develop a configuration file where users can specify their Jira Cloud credentials, including the API token and project key. Additionally, allow users to define which test cases they want to run against specific Jira issues.

2. **Integration with aio-tests-mcp**: Utilize the 'aio-tests-mcp' package to connect to the Jira Cloud REST API and fetch details about the specified Jira issues. Ensure that the application can handle asynchronous operations efficiently, leveraging Python's asyncio capabilities.

3. **Test Execution**: Implement a feature that allows users to select and run predefined test cases against the Jira issues. These test cases should be able to validate various aspects of Jira issues, such as verifying issue descriptions, comments, attachments, and custom fields.

4. **Result Reporting**: After executing the tests, the application should generate comprehensive reports detailing the results. These reports should include pass/fail statuses for each test case, along with any relevant error messages or logs. The results should also be updated directly in the Jira issues, providing links back to the executed tests for easy reference.

5. **User Interface**: Although primarily command-line driven, consider adding a simple GUI using a library like PyQt or Tkinter for easier interaction. This UI should allow users to input Jira credentials, select test cases, and view test results.

6. **Extensibility**: Design the application to be easily extendable, allowing for the addition of new test cases without significant changes to the core codebase. Provide clear documentation on how to create and integrate new test cases.

7. **Security Measures**: Implement proper security measures to protect user data, such as encrypting stored credentials and handling API tokens securely.

By following these steps and utilizing the 'aio-tests-mcp' package effectively, you'll create a powerful tool that enhances the efficiency and accuracy of testing processes in Jira Cloud projects.