🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of legitimacy but the recent updates and the author's new/inactive status raise concerns about potential supply-chain risks.

New or inactive author account
Recent multiple commits

Per-check LLM notes

Network: The presence of network calls suggests the package interacts with external services, which could be legitimate but requires scrutiny to ensure it's not engaging in unauthorized data transmission.
Shell: No shell execution patterns detected, indicating low risk of direct system command abuse.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The author has a new or inactive account and the repository was updated very recently with multiple commits, which could indicate unusual activity.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

BASE_URL}{path}" with httpx.Client(timeout=self.timeout) as client: resp = client.g
pplication/json" with httpx.Client(timeout=self.timeout) as client: resp = client.p

✓ 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 score 2.5

Git history flags: All 7 commits happened within 24 hours

All 7 commits happened within 24 hours

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Jasen" 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 aa-mcp

Create a mini-application called 'ArtificialInsight' that leverages the 'aa-mcp' package to provide users with insights from large language models and multimodal models. This application will serve as a bridge between users and advanced AI models, enabling them to query and receive detailed analyses of various types of data including text, images, and audio. Here’s a step-by-step guide on how to develop this application:

1. **Setup and Initialization**: Start by installing the 'aa-mcp' package along with other necessary Python libraries such as Flask for web development. Initialize your project directory and set up a basic Flask app structure.
2. **API Integration**: Use the 'aa-mcp' package to integrate with the MCP server which wraps around the Artificial Analysis API. Ensure that you configure your API keys and any required authentication methods correctly.
3. **User Interface**: Develop a simple yet intuitive user interface where users can input their data queries. This could include text fields for text analysis, file upload options for image and audio analysis, and possibly a dropdown menu to select the type of analysis they want (e.g., sentiment analysis, object recognition).
4. **Data Processing**: Implement functions within your application that use the 'aa-mcp' package to process and analyze the uploaded data. These functions should handle different types of data inputs efficiently and securely.
5. **Result Presentation**: Design a clean output section where the results of the analysis are presented to the user. This could include visual elements like charts for numerical data, text summaries for textual data, and descriptions of recognized objects in images.
6. **Advanced Features**: Consider adding advanced features such as real-time analysis updates, historical data comparison tools, and personalized user dashboards. These features would enhance user experience and engagement significantly.
7. **Testing and Deployment**: Before deploying your application, thoroughly test all functionalities using both manual and automated testing methods. Once satisfied, deploy your application to a cloud service provider like AWS or Google Cloud Platform.

Throughout the development process, ensure that you adhere to best practices for security, privacy, and accessibility. The 'aa-mcp' package is crucial here as it provides the underlying framework for interfacing with complex AI models, making it easier to focus on building a robust and user-friendly application.