🤖 AI Analysis

Final verdict: SAFE

The package appears to be a straightforward SDK for interfacing with an external API, with minimal risks identified. The metadata suggests it's new and lacks community engagement, but there are no clear signs of malicious intent or activity.

Low network, shell, obfuscation, and credential risks
New package with limited maintainer history

Per-check LLM notes

Network: The network call pattern suggests the package may be using an HTTP client to communicate with a server, which is common but should be reviewed for destination and purpose.
Shell: No shell execution patterns detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package is new, lacks maintainer history, and the repository shows no community engagement.

📦 Package Quality Overall: Low (3.0/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (889 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

15 type-annotated function signatures detected in source

○ Low Multiple Contributors 2.0

Single-author or unverifiable project

1 unique contributor(s) across 8 commits in bch1212/agenttrust-mcp
Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

p("/") self._client = httpx.Client(timeout=timeout) def __enter__(self) -> "AgentTrustClie

✓ 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: gmail.com>

✓ 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 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
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 agentreputation

Create a web-based mini-application called 'AgentTrust Monitor' using Python and the 'agentreputation' package. This application will serve as a tool for users to monitor and manage their trust scores and reputations within the agent-to-agent economy. Here’s a detailed breakdown of what your application should include and how it should function:

1. User Registration and Authentication: Users must be able to register and log in to the application. Use Flask or Django for backend development and integrate with OAuth2 for secure authentication.
2. Trust Score Dashboard: Upon logging in, users should see their current trust score displayed prominently on the dashboard. This score should update dynamically based on recent activities and feedback from other agents.
3. Reputation Management: Allow users to view their reputation details, including positive and negative feedback from other users. Implement features such as adding comments, ratings, and reviews.
4. Feedback Submission: Enable users to submit feedback about other agents they have interacted with. This feedback should contribute to both parties’ trust scores and reputations.
5. Notifications: Set up real-time notifications for users when their trust score changes significantly or when they receive new feedback.
6. API Integration: Utilize the 'agentreputation' package to handle all interactions with the AgentTrust service, including fetching user data, updating trust scores, and managing feedback.
7. Data Visualization: Include charts and graphs to visually represent a user’s trust score history and reputation trends over time.
8. Security Measures: Ensure all user data is securely stored and transmitted. Implement HTTPS encryption and follow best practices for handling sensitive information.
9. Mobile Responsiveness: Design the application to be fully functional and responsive on mobile devices.
10. Documentation: Provide comprehensive documentation for setting up the application, including installation instructions and usage guides.

To get started, begin by setting up a Flask/Django environment and installing the 'agentreputation' package. Then, design the database schema to store user information, feedback, and reputation data. Finally, implement the frontend using HTML/CSS/JavaScript, ensuring a user-friendly interface. Remember to test thoroughly before deploying your application.