alphafold-sovereign-mcp

v1.1.10 suspicious
5.0
Medium Risk

MCP server wrapping AlphaFold DB and 8 other biomedical data sources, with a local SQLite knowledge graph.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits high obfuscation risk and incomplete metadata, raising concerns about its legitimacy and potential hidden intentions. While there is no immediate evidence of malicious activities, these factors combined with the network risk warrant further scrutiny.

  • High obfuscation risk (7/10)
  • Incomplete metadata and author information
Per-check LLM notes
  • Network: The network call pattern is likely for legitimate API interactions, but further investigation is needed to confirm the destination and purpose.
  • Shell: No shell execution patterns were detected, which is expected and indicates no immediate risk from this aspect.
  • Obfuscation: The presence of obfuscated code may indicate an attempt to hide malicious activity or bypass detection mechanisms.
  • Credentials: No clear signs of credential harvesting were found in the provided snippet.
  • Metadata: The author's information is incomplete, and the maintainer has a single package, suggesting potential unreliability.

📦 Package Quality Overall: Medium (7.4/10)

✦ High Test Suite 9.0

Test suite present — 24 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/smaniches/alphafold-sovereign-mcp#readme
  • Detailed PyPI description (15798 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 :: Typed
  • Type checker (mypy / pyright / pytype) referenced in project
  • 672 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 5 unique contributor(s) across 98 commits in smaniches/alphafold-sovereign-mcp
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • lient: self._client = httpx.AsyncClient( base_url=self.config.base_url, head
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • ined] monkeypatch.setitem(__import__("sys").modules, "gudhi", fake_gudhi) def test_compute_tda_finger
✓ 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: topologica.ai>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository smaniches/alphafold-sovereign-mcp appears legitimate

⚠ 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 alphafold-sovereign-mcp 
Develop a Protein Interaction Analysis Tool using the 'alphafold-sovereign-mcp' Python package. This tool will allow researchers and bioinformaticians to query protein structures and interactions from a local database, enhancing their ability to understand complex biological systems. Here’s a detailed step-by-step guide on how to build this application:

1. **Setup Local Environment**: Begin by setting up your development environment. Install Python, pip, and virtualenv. Then, create a new virtual environment and install the 'alphafold-sovereign-mcp' package.

2. **Database Initialization**: Use the 'alphafold-sovereign-mcp' package to initialize a local SQLite database that integrates data from AlphaFold DB and 8 additional biomedical data sources. Ensure the database schema supports efficient querying and indexing of protein sequences, structures, and interaction data.

3. **API Development**: Develop a RESTful API using Flask or Django to interact with the initialized database. Design endpoints for adding, updating, deleting, and retrieving protein data. Include functionality for searching proteins based on sequence similarity, structure, and interaction networks.

4. **User Interface**: Create a simple web interface using HTML, CSS, and JavaScript to interact with the API. The interface should allow users to input protein sequences, browse existing entries, visualize protein structures, and explore interaction networks.

5. **Advanced Features**:
   - **Sequence Alignment**: Implement a feature that allows users to align input sequences against the database to find similar proteins.
   - **Structure Visualization**: Integrate a library like PyMOL or NGL Viewer to render 3D protein structures directly within the web application.
   - **Interaction Networks**: Visualize interaction networks between proteins using a graph visualization library such as D3.js.

6. **Documentation & Testing**: Write comprehensive documentation for both the API and user interface. Conduct thorough testing, including unit tests for the API and integration tests for the web application, to ensure reliability and performance.

7. **Deployment**: Deploy the application to a cloud service provider like AWS or Heroku, ensuring secure access to the database and API endpoints.

By following these steps, you'll create a powerful tool for analyzing and understanding protein interactions, leveraging the extensive data provided by 'alphafold-sovereign-mcp'.

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