AI Analysis
Final verdict: SUSPICIOUS
The package exhibits moderate risk due to incomplete maintainer metadata and a high credential risk score, despite efforts to restrict file access. Further investigation is recommended.
- Incomplete maintainer metadata
- High credential risk
Per-check LLM notes
- Network: The network calls appear to be making HTTP requests possibly for API interactions, which is common but should be scrutinized for unauthorized access or data leakage.
- Shell: The shell execution uses git commands, likely for version control operations. However, unsanctioned use of system commands can pose risks if not properly controlled.
- Obfuscation: No obfuscation patterns detected.
- Credentials: The code attempts to block access to sensitive files like /etc/passwd and enforces restrictions on file paths and extensions, suggesting an attempt to prevent credential harvesting attacks.
- Metadata: The maintainer's author information is incomplete, suggesting a potential lack of transparency or new/inactive account.
Package Quality Overall: Medium (6.6/10)
✦ High
Test Suite
9.0
Test suite present — 23 test file(s) found
Test runner config found: pyproject.toml23 test file(s) detected (e.g. test_property_mapping.py)
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "Documentation" -> https://github.com/alexboissAV/artefact-mcp-server#readmeDetailed PyPI description (26934 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
◈ Medium
Type Annotations
7.0
Partial type annotation coverage
Classifier: Typing :: Typed159 type-annotated function signatures detected in source
◈ Medium
Multiple Contributors
6.0
Limited contributor diversity
2 unique contributor(s) across 22 commits in alexboissAV/artefact-mcp-serverTwo distinct contributors found
Heuristic Checks
Outbound Network Calls
score 4.5
Found 3 network call pattern(s)
) self._client = httpx.Client( headers={ "Authorization": f"BeAPI.""" try: with httpx.Client(timeout=10.0) as client: response = client.post(n thread.""" try: httpx.post( endpoint, json=event_payload,
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
score 2.0
Found 1 shell execution pattern(s)
ne: try: result = subprocess.run( ["git", "-C", str(repo), "rev-parse", "--short"
Credential Harvesting
score 10.0
Found 4 credential access pattern(s)
") # Attack: Try to read /etc/passwd is_valid, error = validate_config_path("/etc/passwd")error = validate_config_path("/etc/passwd") assert not is_valid assert "must be .json" in errin error print("✓ Blocked /etc/passwd (not .json)") # Attack: Try relative path is_validvalidate_config_path("../../../etc/passwd.json") assert not is_valid assert "absolute" in err
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: artefactventures.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository alexboissAV/artefact-mcp-server appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 artefact-mcp
Create a mini-application called 'HubSpot Audit Assistant' that leverages the artefact-mcp package to perform comprehensive audits on a given HubSpot instance. The application should focus on four key areas: Lifecycle Stages Analysis, Pipeline Hygiene Evaluation, AI Readiness Assessment, and Property Governance Review. Each of these functionalities should provide detailed insights into the current state of the HubSpot CRM system, suggesting improvements based on best practices and industry standards. Step-by-Step Guide: 1. Initialize a new Python project and install the artefact-mcp package. 2. Set up authentication with your HubSpot API credentials. 3. Implement a function to analyze lifecycle stages within the CRM, identifying gaps or inconsistencies in the customer journey. 4. Develop another module to evaluate the pipeline hygiene, ensuring that all deals are properly categorized and tracked. 5. Create a feature that assesses the AI readiness of the HubSpot instance, highlighting opportunities for automation and data-driven decision-making. 6. Finally, include a tool for reviewing property governance, ensuring that all custom properties are well-documented and compliant with HubSpot’s guidelines. 7. Integrate these modules into a user-friendly interface where users can select which audit(s) they want to run and view the results. 8. Enhance the application by adding visualizations and summaries for each audit result, making it easier for non-technical users to understand the findings. 9. Consider implementing a feature to schedule regular audits and send notifications about significant changes or issues. Suggested Features: - Customizable audit reports that can be exported as PDFs or shared via email. - A dashboard showing overall health scores and trends over time. - Recommendations for improving specific areas identified during the audit process. - Integration with other tools or services (e.g., Slack) for real-time alerts. Utilizing artefact-mcp Package: - Use artefact-mcp’s free tier features to access the necessary tools for conducting the audits. Specifically, leverage the lifecycle stages analysis, pipeline hygiene evaluation, AI readiness assessment, and property governance review functionalities provided by the package. Ensure that you adhere to the usage limits imposed by the free tier and consider upgrading to the pro tier if more advanced features, such as live revenue intelligence, are required for your application.
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