🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low individual risks but raises concerns due to missing repository and incomplete maintainer profile, suggesting potential issues with transparency and accountability.

Metadata risk due to missing repository and incomplete maintainer profile
Low individual risk scores across other categories

Per-check LLM notes

Network: The observed network call patterns are typical for making HTTP requests and likely part of the package's intended functionality.
Shell: No shell execution patterns were detected.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating safe handling of secrets and credentials.
Metadata: The repository not being found and the maintainer having an incomplete profile raises suspicion.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

ecret).""" async with httpx.AsyncClient(timeout=_HTTP_TIMEOUT) as http: resp = await htt
cached[0] async with httpx.AsyncClient(timeout=_HTTP_TIMEOUT) as http: try:
) -> bool: async with httpx.AsyncClient(timeout=_HTTP_TIMEOUT) as http: resp = await htt
ientView]: async with httpx.AsyncClient(timeout=_HTTP_TIMEOUT) as http: resp = await htt
) -> str: async with httpx.AsyncClient(timeout=_HTTP_TIMEOUT) as http: resp = await htt

✓ 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 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ 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 HeronIntelligence-mcp

Create a fully-functional mini-app called 'AI Butler' that integrates the HeronIntelligence-mcp package to manage and control AI assistant functionalities within a smart home environment. This app should serve as a central hub for various AI-driven tasks such as scheduling, automation, and interaction management.

Step 1: Set up the Project Environment
- Initialize a new Python project.
- Install the HeronIntelligence-mcp package via pip.
- Set up a virtual environment for dependency isolation.

Step 2: Define Core Features
- Implement a scheduling system where users can set reminders and schedule events.
- Develop an automation module that can trigger actions based on time, user commands, or environmental conditions (e.g., turning off lights after sunset).
- Integrate a conversation manager to handle user interactions, providing responses and executing commands based on voice or text input.

Step 3: Utilize HeronIntelligence-mcp Package
- Use the MCP server provided by HeronIntelligence-mcp to manage AI assistant instances.
- Configure the server to support multiple AI assistants if needed, each specialized for different tasks or rooms in the house.
- Implement a command-line interface (CLI) for users to interact with the AI Butler, utilizing the HeronIntelligence-mcp package to process these commands and execute corresponding actions.

Step 4: Enhance User Experience
- Design a simple yet intuitive CLI interface.
- Add voice recognition capabilities using an external service (like Google Speech-to-Text), ensuring seamless integration with the HeronIntelligence-mcp server.
- Incorporate feedback mechanisms so that the AI Butler can learn from user interactions and improve its responses over time.

Step 5: Testing and Deployment
- Thoroughly test the application to ensure all features work as expected.
- Document the setup process and how to use the CLI effectively.
- Deploy the application on a local server for continuous use within the smart home environment.

This project aims to showcase the versatility of the HeronIntelligence-mcp package while providing a practical solution for managing smart homes through AI.