alevlas-random-mcp

v0.1.1 suspicious
4.0
Medium Risk

Minimal MCP server that generates random integers, strings, and UUIDs.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has minimal risks based on the checks performed, but its metadata suggests it might come from a less active or new maintainer, warranting closer scrutiny.

  • Low metadata quality indicating potential lack of maintenance.
  • No significant security risks identified.
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent related to code obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate risk of secret or credential theft.
  • Metadata: The package shows signs of low effort and may be from a new or inactive maintainer, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (3.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. smoke_test.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (1345 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 5.0

Partial type annotation coverage

  • 4 type-annotated function signatures (partial)
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 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

  • Only one version has ever been released — brand new package
  • Author "alevlas" 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 alevlas-random-mcp 
Create a Python-based mini-application named 'RandomDataGenerator' that leverages the 'alevlas-random-mcp' package to generate various types of random data. This application will serve as a simple yet powerful tool for developers who need to quickly generate test data for their projects. Here’s a step-by-step guide on how to build this application:

1. **Project Setup**: Initialize your project by setting up a virtual environment and installing the required packages, including 'alevlas-random-mcp'.
2. **Application Structure**: Design the application structure with clear separation between data generation logic and user interface.
3. **Data Generation Logic**: Implement functions that use 'alevlas-random-mcp' to generate random integers, strings, and UUIDs. Each function should accept parameters like length, range, and format to customize the output.
4. **User Interface**: Develop a command-line interface (CLI) that allows users to interact with the application easily. Users should be able to select the type of data they want to generate, specify parameters, and view the generated data.
5. **Enhanced Features**: Consider adding features such as saving the generated data to a file, generating multiple entries at once, and providing an option to generate data based on specific patterns or distributions.
6. **Testing**: Write tests to ensure that each function works as expected and handles edge cases appropriately.
7. **Documentation**: Provide clear documentation on how to install and use the application, including examples of usage scenarios.
8. **Deployment**: Package the application for easy distribution and deployment. Consider hosting it on PyPI for others to easily install via pip.

By following these steps, you'll create a versatile tool that can be used for testing, prototyping, and other scenarios where random data generation is needed.

💬 Discussion Feed

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