awslabs.aws-healthomics-mcp-server

v0.0.39 safe
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package appears to be legitimate based on its description and the low scores across various risk categories. While there is a moderate concern regarding potential credential harvesting, this is likely due to legitimate AWS service interactions.

  • No network calls detected
  • No shell execution patterns
  • Potential credential risk due to environment variable access
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 no immediate signs of executing system commands.
  • Obfuscation: Base64 decoding is commonly used for data serialization and not necessarily indicative of malicious activity.
  • Credentials: The code snippet accessing environment variables could potentially be exploited for credential harvesting, but it may also be a legitimate use for AWS service interactions.
  • Metadata: The maintainer has only one package, which might indicate a new or less active account but does not strongly suggest malicious intent.

📦 Package Quality Overall: Medium (7.0/10)

✦ High Test Suite 9.0

Test suite present — 30 test file(s) found

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

Some documentation present

  • Documentation URL: "docs" -> https://awslabs.github.io/mcp/servers/aws-healthomics-mcp-se
  • Detailed PyPI description (35444 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
  • 206 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 10.0

Found 6 obfuscation pattern(s)

  • try: decoded = base64.b64decode(token_str.encode('utf-8')).decode('utf-8') token
  • prefix decoded = base64.b64decode(encoded.encode('utf-8')).decode('utf-8') token_d
  • oded bytes """ return base64.b64decode(data) def create_aws_client( service_name: str, re
  • text_content}.""" data = base64.b64decode(b64) with zipfile.ZipFile(BytesIO(data)) as zf:
  • amespace packages. __path__ = __import__('pkgutil').extend_path(__path__, __name__) # Copyright Amazon.com, In
  • 0).map( lambda b: __import__('base64').b64encode(b).decode() ), ) @pytest.mark.asy
Shell / Subprocess Execution

No shell execution patterns detected

Credential Harvesting score 10.0

Found 6 credential access pattern(s)

  • egion name """ return os.environ.get('AWS_REGION', DEFAULT_REGION) def get_omics_service_name() -> s
  • Paths like 'foo/bar/../../etc/passwd' where '..' is not caught by the simple string pref
  • date_local_path('foo/bar/../../etc/passwd') @pytest.mark.asyncio @patch('awslabs.aws_healtho
  • sted traversal like 'a/b/../../etc/passwd'.""" with pytest.raises(ValueError, match='Path con
  • validate_local_path('a/b/../../etc/passwd') def test_backslash_traversal(self) -> None:
  • idate_local_path('dir/../../../etc/passwd') def test_backslash_dot_dot_windows(self) -> None:
Typosquatting

No typosquatting candidates detected

Registered Email Domain

Email domain looks legitimate: users.noreply.github.com>

Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://`
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.

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