🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no evidence of malicious intent or activity. The primary concern lies in the repository's low activity and limited maintainer history, which might affect long-term support and maintenance.

Low network, shell, obfuscation, and credential risks.
Repository has low activity and limited maintainer history.

Per-check LLM notes

Network: The network call to localhost suggests an internal health check which is generally benign.
Shell: No shell execution patterns detected.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, indicating low risk of malicious activity.
Metadata: The repository's low activity and the maintainer's limited history suggest potential risks.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

try: resp = urllib.request.urlopen("http://localhost:8000/health", timeout=2)

✓ 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

Email domain looks legitimate: meok.ai>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with accessibility-ai-mcp

Develop a web-based accessibility checker tool using Python's 'accessibility-ai-mcp' package. This tool will help users ensure their website content adheres to accessibility standards, particularly focusing on color contrast, alt text suggestions, and heading hierarchy checks. The application should have the following functionalities:

1. **Homepage**: A clean, user-friendly interface where users can input the URL of the webpage they want to check.
2. **Color Contrast Checker**: Utilize the 'accessibility-ai-mcp' package to analyze the color contrast of the webpage. Display a report indicating which elements pass and fail the WCAG (Web Content Accessibility Guidelines) standards for color contrast.
3. **Alt Text Suggestion Tool**: Automatically generate suggestions for missing or inadequate alt text for images on the webpage. This feature should provide users with options to accept, modify, or reject the suggested alt texts.
4. **Heading Hierarchy Analysis**: Evaluate the structure of headings (H1, H2, etc.) on the page to ensure they follow a logical and accessible order. Provide feedback on any issues found and suggest improvements.
5. **Report Generation**: After running all checks, compile a comprehensive report summarizing the findings. Include recommendations for fixing any identified accessibility issues.
6. **User Interface Enhancements**: Implement responsive design principles to ensure the tool is accessible and usable on various devices. Also, include keyboard navigation support and screen reader compatibility.

To achieve these functionalities, you will need to integrate the 'accessibility-ai-mcp' package into your project. This involves setting up a Python environment, installing the necessary dependencies, and configuring the package to interact with web content. Additionally, consider developing a backend service to handle the processing of webpages and a frontend interface for user interaction.