🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows suspicious metadata activities which could indicate potential risks, but lacks evidence of harmful actions such as shell execution, obfuscation, or credential theft.

Suspicious metadata risk
No direct malicious code found

Per-check LLM notes

Network: The network call pattern indicates the package is likely performing some form of HTTP requests, possibly for legitimate purposes like fetching updates or communicating with a server. Further investigation into the package's functionality is recommended.
Shell: No shell execution patterns were detected, indicating low risk of direct system command execution from the package.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: Suspicious activity around the git repository and maintainer history suggest potential risk.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

try: async with httpx.AsyncClient(timeout=httpx.Timeout(15.0, connect=10.0)) as client:

✓ 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: agentdomain.cloud>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 5.0

Git history flags: Single contributor with only 3 commit(s) — possibly throwaway account

Single contributor with only 3 commit(s) — possibly throwaway account
All 3 commits happened within 24 hours

⚠ 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 agentdomain-mcp

Create a domain management console using the 'agentdomain-mcp' Python package. This console will serve as a user-friendly interface for registering, buying, and managing domains through an AI agent. Your task is to develop a web-based application that integrates with 'agentdomain-mcp' to provide these functionalities. Here’s a detailed outline of the steps and features your application should include:

1. **Setup Environment**: Ensure you have Python installed along with Flask or Django for web development. Install the 'agentdomain-mcp' package via pip.
2. **User Authentication**: Implement user registration and login functionality. Users should be able to create accounts and securely log in.
3. **Domain Registration**: Create a form where users can enter details to register new domains. Use 'agentdomain-mcp' to handle the backend process of registering the domain.
4. **Domain Purchase**: Integrate a feature allowing users to purchase available domains directly from the console. Again, use 'agentdomain-mcp' to facilitate the purchase process.
5. **Domain Management**: Allow users to manage their registered domains (e.g., renew, transfer, delete). Ensure 'agentdomain-mcp' is utilized for these actions.
6. **Search Functionality**: Enable users to search for available domains. 'agentdomain-mcp' should power this search feature.
7. **Notifications**: Implement a notification system to inform users about domain expiration dates, renewal status, etc.
8. **API Integration**: Consider exposing an API so other applications can interact with your domain management console programmatically.
9. **Documentation**: Write comprehensive documentation explaining how to use the application, including setup instructions and API usage.

Your application should demonstrate proficiency in using 'agentdomain-mcp', showcasing its capabilities while providing a seamless user experience. Additionally, ensure your code is well-documented and follows best practices in web development.