atlaspi-mcp

v0.9.0 suspicious
4.0
Medium Risk

MCP server for AtlasPI — historical geographic database for AI agents

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to its new account status and missing author details, raising concerns about potential malicious intent despite no direct evidence of harmful behavior.

  • Metadata risk due to new account and missing author information
  • Potential network interactions with 'atlaspi.test' need further verification
Per-check LLM notes
  • Network: The network calls are likely intended for legitimate API interactions with the 'atlaspi.test' domain, but further investigation is needed to confirm their purpose and ensure no unauthorized data transfer.
  • Shell: No shell execution patterns were detected in the provided code snippet.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows some red flags such as an author with a missing name and a new account, but there's no clear evidence of typosquatting or other malicious intent.

📦 Package Quality Overall: Medium (6.4/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/Soil911/AtlasPI#readme
  • Detailed PyPI description (8622 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
  • 107 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in Soil911/AtlasPI
  • Single author but highly active (100 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • self._client = client or httpx.AsyncClient( base_url=self.base_url, timeout=tim
  • t(handler) httpx_client = httpx.AsyncClient( base_url="https://atlaspi.test", transport=
✓ Code Obfuscation

No obfuscation patterns detected

✓ 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: cra-srl.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ 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 atlaspi-mcp 
Create a geospatial data analysis tool using the 'atlaspi-mcp' Python package, which acts as a server for the AtlasPI historical geographic database designed for AI agents. This tool will allow users to query historical geographic data and visualize it on a map. Here are the steps and features for your project:

1. **Setup**: Install the necessary packages including 'atlaspi-mcp'. Ensure you have a basic understanding of Python and Flask for web development.
2. **Server Configuration**: Use 'atlaspi-mcp' to set up a server that connects to the AtlasPI database. Configure endpoints to handle queries for different types of historical geographic data.
3. **User Interface**: Develop a simple web interface where users can input parameters such as date range, location coordinates, and data type (e.g., population density, land use). The UI should be user-friendly and responsive.
4. **Data Retrieval**: Implement backend logic to retrieve historical geographic data based on user inputs. Utilize 'atlaspi-mcp' functionalities to interact with the AtlasPI database efficiently.
5. **Data Visualization**: Integrate a mapping library like Leaflet.js or Mapbox to display retrieved data on an interactive map. Users should be able to see changes over time through animations or layer overlays.
6. **Advanced Features**: Consider adding advanced features such as filtering options, zoom levels, and support for different map projections. Allow users to export visualizations as images or shareable links.
7. **Testing & Deployment**: Test your application thoroughly to ensure all features work as expected. Deploy your app to a cloud service provider like Heroku or AWS for public access.

Your goal is to create a fully functional, user-friendly tool that leverages the capabilities of 'atlaspi-mcp' to provide insightful geospatial data analysis and visualization.

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