awslabs.healthimaging-mcp-server

v0.0.7 safe
3.0
Low Risk

An AWS Labs Model Context Protocol (MCP) server for HealthImaging

🤖 AI Analysis

Final verdict: SAFE

The package shows low risk indicators with no network calls, shell executions, or credential harvesting attempts. While there is some obfuscation and metadata risks, these do not strongly suggest malicious intent.

  • No network calls detected
  • No shell execution patterns
  • Low credential risk
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external services.
  • Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
  • Obfuscation: The observed pattern is commonly used for extending package paths and is not indicative of malicious obfuscation.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The author has only one package, which may indicate a new or less active account, raising slight suspicion.

📦 Package Quality Overall: Medium (7.0/10)

✦ High Test Suite 9.0

Test suite present — 5 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 5 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "docs" -> https://awslabs.github.io/mcp/servers/healthimaging-mcp-serv
  • Detailed PyPI description (17249 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 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 119 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 42 unique contributor(s) across 100 commits in awslabs/mcp
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • amespace packages. __path__ = __import__('pkgutil').extend_path(__path__, __name__) # Copyright Amazon.com, In
✓ 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: users.noreply.github.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository awslabs/mcp appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Amazon Web Services" 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 awslabs.healthimaging-mcp-server 
Create a medical imaging analysis tool using the 'awslabs.healthimaging-mcp-server' Python package. This tool will allow healthcare professionals to upload DICOM files, manage them through a simple web interface, and retrieve model predictions based on these images. The application should include the following functionalities:

1. User Authentication: Implement basic user authentication so only authorized users can access the system.
2. DICOM File Upload: Allow users to upload DICOM files through a web form.
3. Image Management: Provide functionality to view, delete, and search for uploaded DICOM images.
4. Model Prediction: Utilize the 'awslabs.healthimaging-mcp-server' package to run pre-trained models on the uploaded DICOM files and display the results to the user.
5. Reporting: Enable users to generate and download reports based on the model predictions.

The 'awslabs.healthimaging-mcp-server' package will be used to handle the communication between the DICOM files and the pre-trained models hosted on AWS. It simplifies the process of sending DICOM data to the models and receiving the predictions back. Your task is to integrate this package into your application, ensuring that it can seamlessly interact with the models and provide useful insights from the medical images.

💬 Discussion Feed

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