🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no immediate signs of malicious activity but raises concerns due to missing repository and sparse maintainer information, potentially indicating a lack of transparency.

Metadata risk noted due to missing repository and sparse maintainer details.
No direct malicious activities detected in code.

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communication.
Shell: No shell execution patterns detected, indicating the package likely does not execute system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The repository is not found, and the maintainer information is sparse, raising concerns about potential malicious intent.

📦 Package Quality Overall: Low (3.2/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (4109 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Development Status classifier >= Beta

◈ Medium Type Annotations 5.0

Partial type annotation coverage

53 type-annotated function signatures detected in source

○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

GitHub API error: 404

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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: agimus.ai>

✓ 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 agimus-mcp

Create a mini-application called 'OntologyLinker' that integrates the Agimus data platform with a user-defined ontology. This application will serve as a bridge between various AI coding tools and the specified ontology, enabling users to query and manipulate data according to their needs. The app should have the following functionalities:

1. **Initialization**: Upon startup, the application should connect to the Agimus data platform using the 'agimus-mcp' package. It must authenticate with the necessary credentials provided by the user.
2. **Ontology Management**: Users should be able to upload, modify, and delete ontologies through a simple command-line interface (CLI). The application should validate the ontology structure against predefined rules to ensure consistency.
3. **Query Interface**: Implement a robust query system that allows users to ask questions about the ontology data using natural language processing (NLP) techniques. The queries should be translated into appropriate SPARQL queries and executed on the connected Agimus data platform.
4. **Data Visualization**: Integrate a basic visualization tool that can generate charts and graphs based on the queried data. Users should be able to choose from different chart types (e.g., bar charts, pie charts) and customize them.
5. **Documentation and Help**: Include comprehensive documentation and a help section within the CLI to guide users through the setup process and usage of the application.

To utilize the 'agimus-mcp' package, you'll need to import it at the beginning of your Python script. Use its methods to establish a connection with the Agimus data platform, manage the ontology lifecycle, execute SPARQL queries, and handle responses. Ensure that error handling is implemented to provide informative messages when issues arise during the operation of the application.