autonomath-mcp

v0.5.0 suspicious
4.0
Medium Risk

REST + MCP context-compression layer for Japanese institutional public data. jpcite turns long PDFs, official pages, and search results into compact Evidence Packets with source URLs, fetched timestamps, known gaps, and compatibility/exclusion rules before downstream AI agents draft answers. 3 yen/billable unit metered (3.30 tax-incl), anonymous 3/day per IP free.

πŸ€– AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of potential credential misuse and has an incomplete maintainer profile, raising concerns about its legitimacy and safety.

  • Credential risk due to possible misuse of API_KEY_SALT
  • Incomplete maintainer profile
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
  • Shell: No shell executions detected, indicating no immediate risk of command injection or execution.
  • Obfuscation: No obfuscation patterns detected.
  • Credentials: The observed pattern could be part of a legitimate API key handling mechanism, but the lack of context around how the API_KEY_SALT is used warrants caution.
  • Metadata: The maintainer has an incomplete profile and appears to be new or inactive, raising some suspicion but not conclusive evidence of malice.

πŸ“¦ Package Quality Overall: Medium (6.4/10)

β—ˆ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
β—ˆ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://jpcite.com/docs/
  • Detailed PyPI description (32301 chars)
β—‹ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
β—ˆ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 385 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in shigetosidumeda-cyber/autonomath-mcp
  • Small but multi-author team (3–4 contributors)

πŸ”¬ Heuristic Checks

βœ“ Outbound Network Calls

No suspicious network call patterns found

βœ“ Code Obfuscation

No obfuscation patterns detected

βœ“ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • gate enforced).""" salt = os.getenv("API_KEY_SALT", "") or "" # An empty salt in tests / CI is fine
βœ“ Typosquatting

No typosquatting candidates detected

βœ“ Registered Email Domain

Email domain looks legitimate: bookyou.net>

βœ“ Suspicious Page Links

All external links appear legitimate

βœ“ Git Repository History

Repository shigetosidumeda-cyber/autonomath-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)
βœ“ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

πŸ’‘ AI App Starter Prompt

Use this prompt to build a project with autonomath-mcp 
Create a Python-based mini-application called 'InstitutionalDataSummarizer' that leverages the 'autonomath-mcp' package to process and summarize Japanese institutional public data from various sources like PDFs and official websites. The application should perform the following steps:

1. **Initialization**: Allow users to input a URL or upload a file containing institutional data.
2. **Data Fetching**: Use 'autonomath-mcp' to fetch the raw data from the provided source. Ensure that the application handles different types of content, such as PDFs and web pages.
3. **Context Compression**: Apply 'autonomath-mcp' to compress the fetched data into compact Evidence Packets. Each packet should include the original source URL, timestamp of fetching, any identified gaps in the data, and rules regarding compatibility or exclusion.
4. **Data Summarization**: Implement a feature where the summarized evidence packets are further condensed into human-readable summaries. This summary should highlight key points, provide a brief overview, and maintain accuracy based on the compressed data.
5. **Output Presentation**: Present the summarized data in a user-friendly format, either as a downloadable text file or a formatted HTML page.
6. **Cost Management**: Since 'autonomath-mcp' charges for usage, ensure the application tracks the number of billable units consumed and alerts users if they are nearing their daily free limit (3 billable units).
7. **User Interface**: Develop a simple command-line interface (CLI) for interacting with the application. Consider adding basic error handling and validation checks for user inputs.

**Suggested Features**:
- Option to save the summarized output directly to a specified directory.
- Enhanced logging to track the application's operations and any issues encountered during processing.
- Integration with popular cloud storage services for saving the output files.
- Support for batch processing multiple files or URLs at once.

**How 'autonomath-mcp' is Utilized**:
- The package's REST API is used to fetch and process the raw data.
- Context compression is achieved through 'autonomath-mcp', which intelligently reduces large datasets into manageable, structured packets.
- These packets serve as the foundation for generating accurate and concise summaries of the institutional data.

πŸ’¬ Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!