artifacta-mcp

v1.0.1 suspicious
4.0
Medium Risk

Artifacta MCP server — exposes the Artifacta artifact store to AI agents via the Model Context Protocol

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate metadata risk due to potential new or inactive maintainer activity and a missing repository, which raises suspicion. However, other risk factors are low, making the overall risk score moderate.

  • Moderate metadata risk
  • Potential new or inactive maintainer
  • Missing repository
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands.
  • Obfuscation: Base64 decoding with validation might be used for obfuscation, but it could also be a legitimate use such as handling encoded configuration data.
  • Credentials: No clear evidence of credential harvesting patterns detected.
  • Metadata: The package shows signs of potential new or inactive maintainer activity and a non-existent repository, raising concerns.

📦 Package Quality Overall: Low (4.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.artifacta.io/mcp
  • Detailed PyPI description (11247 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

  • 73 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 score 2.0

Found 1 obfuscation pattern(s)

  • try: file_bytes = base64.b64decode(a["content"], validate=True) except (ValueError, Typ
✓ 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: artifacta.io>

✓ 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 artifacta-mcp 
Create a mini-application named 'Artifacta-AI-Connector' that serves as a bridge between AI agents and an Artifacta artifact store using the Python package 'artifacta-mcp'. This application will enable AI agents to interact with the Artifacta artifact store via the Model Context Protocol (MCP). Here’s a step-by-step guide on what the application should achieve and how it will utilize the 'artifacta-mcp' package:

1. **Setup**: Begin by setting up a virtual environment for your project and installing the necessary dependencies, including the 'artifacta-mcp' package.
2. **Configuration**: Configure the application to connect to an Artifacta artifact store by providing the required credentials and endpoints. This step ensures that your application can communicate with the Artifacta store effectively.
3. **API Development**: Develop a RESTful API that exposes key functionalities of the 'artifacta-mcp' package. These functionalities include uploading artifacts, downloading artifacts, listing artifacts, and managing metadata associated with artifacts.
4. **Authentication & Authorization**: Implement authentication and authorization mechanisms to ensure secure access to the Artifacta artifact store. This could involve token-based authentication or OAuth2, depending on the requirements.
5. **Documentation**: Provide comprehensive documentation for the API, detailing each endpoint, its purpose, request/response formats, and any prerequisites or limitations.
6. **Testing**: Conduct thorough testing of the application to ensure all functionalities work as expected. This includes unit tests, integration tests, and possibly load/stress tests if the application is expected to handle high traffic.
7. **Deployment**: Prepare the application for deployment by containerizing it using Docker, ensuring that it can run in different environments seamlessly.
8. **Monitoring & Logging**: Set up monitoring and logging capabilities to track the performance and health of the application in real-time, allowing for quick troubleshooting and optimization.

Suggested Features:
- Support for multiple artifact types (e.g., images, documents, models).
- Advanced search functionality based on metadata tags.
- Versioning support for artifacts to manage different versions of the same asset.
- Webhooks for notifying users/agents about changes in the artifact store.

The 'artifacta-mcp' package is crucial in this project as it provides the underlying infrastructure for exposing the Artifacta artifact store to AI agents. It simplifies the process of integrating with the Artifacta store and offers robust functionalities that can be leveraged to build a powerful and flexible application.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!