🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in terms of network, shell, obfuscation, and credential activities but has a moderate risk due to poor metadata quality and low maintainer activity, suggesting potential issues with its legitimacy.

Moderate metadata risk
Low maintainer activity

Per-check LLM notes

Network: The network calls seem to be part of normal backend and agent service interactions, which is typical for server-side applications.
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 shows signs of low maintainer activity and poor metadata quality, raising concerns about its legitimacy.

📦 Package Quality Overall: Low (3.6/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

Test runner config found: pyproject.toml
1 test file(s) detected (e.g. test_mcp_tools.py)

○ Low Documentation 1.0

No documentation detected

No documentation URL, doc files, or meaningful description found

○ 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

78 type-annotated function signatures detected in source

○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

ttings self._client = httpx.AsyncClient( base_url=settings.backend_api_base_url,
self._agent_client = httpx.AsyncClient( base_url=settings.agent_service_base_url,

✓ 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

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 8.0

4 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)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with ai-ecom-mcp-server

Your task is to develop a fully-functional mini-application called 'EcoAIHelper' using the Python package 'ai-ecom-mcp-server'. EcoAIHelper will serve as an AI-driven toolset designed specifically for e-commerce operations, enabling users to perform various tasks such as product recommendation, customer behavior analysis, and inventory management optimization. Here’s a detailed breakdown of what your application should achieve and how you’ll utilize the 'ai-ecom-mcp-server' package:

1. **Product Recommendation Engine**: Implement a feature where the user can input a set of products they've recently viewed or purchased. The app should then recommend additional products based on AI-driven analysis of the user's browsing and purchase history.
2. **Customer Behavior Analysis Tool**: Allow users to upload customer interaction data (e.g., clicks, views, purchases). The app should analyze this data to provide insights into customer behavior patterns, such as frequently viewed categories, best-selling items, and peak shopping times.
3. **Inventory Management Optimization**: Provide a function where users can input their current inventory levels and sales forecasts. The app should suggest optimal inventory levels to maintain based on historical sales data and predicted demand.
4. **Integration with 'ai-ecom-mcp-server'**: Use the 'ai-ecom-mcp-server' package to connect these functionalities to an AI backend service. This involves setting up a connection to the MCP server, utilizing its AI tools for data processing and analysis, and ensuring secure data transmission.
5. **User Interface**: Develop a simple yet effective web-based UI where users can interact with the app, input data, and view results. Ensure the interface is intuitive and easy to navigate.
6. **Security Measures**: Incorporate security measures such as data encryption during transmission and storage, user authentication, and access control to protect sensitive information.
7. **Documentation and User Guide**: Prepare comprehensive documentation and a user guide that explains how to use each feature of the app, including setup instructions, API usage guidelines, and troubleshooting tips.

By completing this project, you'll not only gain hands-on experience with the 'ai-ecom-mcp-server' package but also create a valuable tool for e-commerce businesses looking to enhance their operational efficiency through AI.