🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in terms of network calls, shell execution, obfuscation, and credential handling. However, the metadata risk score is elevated due to the repository's lack of engagement and minimal author activity, raising concerns about potential malicious intent.

Elevated metadata risk score
Minimal author activity and repository engagement

Per-check LLM notes

Network: The network call to localhost is likely for local health checks and not indicative of malicious activity.
Shell: No shell execution patterns were detected, indicating no immediate risk from this aspect.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The repository's lack of engagement and the author's minimal activity raise concerns about potential malicious intent.

📦 Package Quality Overall: Low (4.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

1 test file(s) detected (e.g. test_server.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (4154 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

16 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 6.0

Limited contributor diversity

2 unique contributor(s) across 43 commits in CSOAI-ORG/ai-gateway-mcp
Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

try: resp = urllib.request.urlopen("http://localhost:8000/health", timeout=2)

✓ 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: meok.ai>

✓ 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 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 ai-gateway-mcp

Create a Python-based mini-application named 'AI Navigator' that leverages the 'ai-gateway-mcp' package to streamline interactions with various AI models. The application should serve as a user-friendly interface for managing and utilizing different AI services efficiently. Here’s a detailed plan on how to develop it:

1. **Project Setup**: Initialize a new Python project. Ensure you install the 'ai-gateway-mcp' package from PyPI.
2. **User Interface Design**: Develop a simple command-line interface (CLI) or a basic web interface using Flask. This will allow users to interact with the AI services more intuitively.
3. **Integration with ai-gateway-mcp**:
   - Use the 'route_request' capability to direct requests to appropriate AI models based on user input.
   - Implement a feature to list available AI models through 'list_models'. Display these options to the user in a readable format.
   - Integrate the 'cost_estimator' function to provide users with an estimate of the costs associated with their chosen AI model(s).
4. **Core Features**:
   - **Model Selection**: Allow users to choose from a list of available AI models and specify the type of task they want to perform (e.g., text generation, image processing).
   - **Request Routing**: Automatically route the user's request to the selected AI model via the 'route_request' functionality.
   - **Cost Estimation**: Before executing any task, provide a cost estimation for the user's convenience.
5. **Enhancements**:
   - Add support for user authentication and authorization to ensure secure access to the AI services.
   - Implement logging to track user activities and model usage for future analytics.
   - Develop a feature to save and manage user preferences, such as preferred AI models and cost thresholds.
6. **Testing & Deployment**: Thoroughly test the application for functionality and performance. Once satisfied, deploy it either as a standalone CLI tool or as a web application accessible over the internet.

This project aims to showcase the versatility and ease-of-use of the 'ai-gateway-mcp' package while providing a valuable tool for anyone looking to explore and utilize AI services.