awslabs.healthlake-mcp-server

v0.0.16 suspicious
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some potential risks, particularly concerning network calls and obfuscation techniques, which require further scrutiny. However, there's no concrete evidence of malicious intent.

  • Network calls made via httpx client need clarification.
  • Observed obfuscation pattern requires additional investigation.
Per-check LLM notes
  • Network: Network calls using httpx client are common and may be legitimate for API interactions, but further investigation is needed to confirm the purpose of these calls.
  • Shell: No shell execution patterns were detected, which suggests no immediate risk from command execution.
  • Obfuscation: The observed pattern is commonly used for extending the search path for packages and is not inherently malicious.
  • Credentials: No suspicious patterns indicating credential harvesting were found.
  • Metadata: The author has only one package, which may indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 19 test file(s) found

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

Some documentation present

  • Documentation URL: "docs" -> https://awslabs.github.io/mcp/servers/healthlake-mcp-server/
  • Detailed PyPI description (16639 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

  • 52 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 score 4.5

Found 3 network call pattern(s)

  • anges. async with httpx.AsyncClient(follow_redirects=False) as client: if next_t
  • ) async with httpx.AsyncClient(follow_redirects=False) as client: if next_t
  • st()) async with httpx.AsyncClient(follow_redirects=False) as client: response
⚠ 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: amazon.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.healthlake-mcp-server 
Create a healthcare data management mini-app using the 'awslabs.healthlake-mcp-server' package. This app will serve as a bridge between healthcare providers and patients, enabling secure and efficient sharing of medical records. Here’s how you will build it:

1. **Setup**: Begin by setting up your development environment with Python 3.x and installing the 'awslabs.healthlake-mcp-server' package. Ensure you have an AWS account with access to Amazon HealthLake.

2. **Authentication**: Implement a secure login system where users (healthcare providers and patients) can authenticate themselves. Use OAuth 2.0 for authentication purposes, ensuring compliance with HIPAA standards.

3. **Data Ingestion**: Utilize the 'awslabs.healthlake-mcp-server' package to ingest medical records from various sources into Amazon HealthLake. These records could include patient demographics, medical history, lab results, etc.

4. **Search Functionality**: Develop a robust search functionality that allows users to query their medical records based on specific criteria such as date range, type of record, or keyword searches.

5. **Privacy Controls**: Integrate privacy controls that allow patients to specify which parts of their medical records they want to share and with whom. This feature should also comply with GDPR and HIPAA regulations.

6. **Notifications**: Implement a notification system that alerts patients when new records are added or updated, or when a healthcare provider requests access to their records.

7. **Visualization Tools**: Add visualization tools that help patients better understand their medical data. For example, charts showing trends in blood pressure readings over time.

8. **Feedback Mechanism**: Include a feedback mechanism where patients can rate their experience with the app and suggest improvements.

9. **Testing & Deployment**: Thoroughly test all functionalities of the app to ensure reliability and security. Deploy the app using AWS services, ensuring it scales well with increasing user numbers.

The 'awslabs.healthlake-mcp-server' package will be crucial in handling the ingestion and management of medical records within Amazon HealthLake, ensuring that the data is stored securely and efficiently.

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