a-share-factor-mcp

v0.1.1 safe
3.0
Low Risk

A-Share Factor Screening & Strategy Health MCP Server — 6 tools for PE/PB/small-cap/dividend/momentum/multi-factor

🤖 AI Analysis

Final verdict: SAFE

The package has minimal risks across all categories with no network calls, shell executions, or obfuscations detected. Although it has low maintainer activity and poor metadata quality, there are no clear signs of malicious intent.

  • Low network and shell risk
  • No obfuscation or credential harvesting patterns
  • Poor metadata quality and low maintainer activity
Per-check LLM notes
  • Network: No network calls detected, which is normal for packages not requiring external data.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows low maintainer activity and poor metadata quality, but there are no clear signs of malicious intent.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with a-share-factor-mcp 
Create a financial analysis tool named 'A-Share Explorer' using the Python package 'a-share-factor-mcp'. This tool will enable users to analyze Chinese stock market data based on six key factors: Price-to-Earnings ratio (PE), Price-to-Book ratio (PB), Market Capitalization, Dividend Yield, Momentum, and Multi-Factor analysis. The application should provide a user-friendly interface where users can input specific dates and select which factors they wish to analyze. Additionally, the tool should offer visualizations of the selected factors over time and allow users to save their analysis results for future reference. Here are the steps and features for building 'A-Share Explorer':

1. **Setup**: Install 'a-share-factor-mcp' and other necessary packages such as pandas, matplotlib, and streamlit.
2. **Data Fetching**: Utilize 'a-share-factor-mcp' to fetch historical data based on user-defined criteria.
3. **Analysis Module**: Implement modules for each of the six factors (PE, PB, Market Cap, Dividend Yield, Momentum, Multi-Factor).
4. **Visualization**: Create interactive plots for each factor allowing users to zoom in/out and explore trends.
5. **User Interface**: Design a simple yet effective UI using Streamlit to facilitate easy interaction with the tool.
6. **Save Results**: Enable users to export their analysis results in CSV format for offline review.
7. **Documentation**: Provide comprehensive documentation explaining how to use the tool and interpret the results.