🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to potential unsafe shell command execution and network interactions with external services. While there is no clear evidence of malicious intent, the low maintainer activity and poor metadata quality raise concerns.

Shell risk due to execution of potentially unsafe shell commands.
Network risk from interactions with external services.

Per-check LLM notes

Network: Network calls appear to be interacting with external services which could be legitimate if the package is designed for such interactions, but requires further investigation into their purpose.
Shell: Execution of shell commands, especially with potentially unsafe directory configurations, raises concerns about unauthorized access or malicious behavior.
Obfuscation: The pattern matches phrases related to authorization actions, which could indicate an attempt at hiding such actions but could also be part of normal logging or auditing practices.
Credentials: No credential harvesting patterns were detected in the provided code snippet.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, but lacks clear indicators of malicious intent.

📦 Package Quality Overall: Low (4.4/10)

✦ 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_publisher_setup.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (74339 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

215 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 7.5

Found 5 network call pattern(s)

ICAL_MCP_NAME}) request = urllib.request.Request(f"{SEARCH_URL}?{query}", headers={"User-Agent": "ai-
inal"}) try: with urllib.request.urlopen(request, timeout=timeout) as response: b
dict[str, Any]: request = urllib.request.Request(PYPI_JSON_URL, headers={"User-Agent": "ai-objective-
30a2"}) try: with urllib.request.urlopen(request, timeout=timeout) as response: p
30a2"}) try: with urllib.request.urlopen(request, timeout=timeout) as response: b

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

e.I) ACTION_AUTH_PATTERN = re.compile(r"\b(action authorized|authorized to execute|authorized to deploy|authorized to merge)\b", re.I) de

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

try: completed = subprocess.run( command, cwd=repo_root(),
try: completed = subprocess.run( ["git", "-c", "safe.directory=C:/Users/Isometri
try: completed = subprocess.run(command, cwd=repo_root(), timeout=timeout, check=False)

✓ 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 6.0

3 maintainer concern(s) found

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-objective-index

Develop a web-based application named 'AI Objective Selector' using Python and the 'ai-objective-index' package. This application will serve as a tool for users to evaluate and select the most suitable AI tools, APIs, SaaS products, and MCP servers based on their specific objectives and requirements. Here's a step-by-step guide on how to build this application:

1. **Setup**: Begin by setting up your development environment. Ensure you have Python installed along with Flask for the backend and React for the frontend if you choose a full-stack approach.
2. **Installation**: Install the 'ai-objective-index' package and any other necessary Python packages such as Flask for the server-side logic.
3. **Database Integration**: Set up a database to store user preferences and selections. SQLite can be used for simplicity during development.
4. **Backend Development**:
- Create API endpoints to interact with the 'ai-objective-index' package. These endpoints should allow for querying the available AI tools, APIs, SaaS products, and MCP servers based on user inputs.
- Implement functionality to rank these options based on their fit to the user's specified objectives using the package's core engine.
5. **Frontend Development**:
- Design an intuitive interface where users can input their goals and criteria for selecting an AI solution.
- Display the ranked list of AI solutions provided by the backend, highlighting key features and compatibility with the user's objectives.
6. **User Interface Enhancements**:
- Include filters and sorting options to refine search results.
- Add a comparison feature allowing users to side-by-side compare selected AI solutions.
7. **Testing and Deployment**:
- Thoroughly test the application to ensure all features work as expected.
- Deploy the application to a cloud service like Heroku or AWS.
8. **Documentation and Support**:
- Write documentation detailing how to use the application effectively.
- Provide support channels for users who encounter issues.

The 'ai-objective-index' package will be crucial in powering the application's ability to assess and rank AI solutions based on user-defined criteria. It provides the core engine needed to match user objectives with the best possible AI tools, making it easier for individuals and businesses to find the right AI solutions for their needs.