aws-s3-explorer-mcp

v0.1.4 suspicious
4.0
Medium Risk

An open-source Model Context Protocol (MCP) server for Amazon S3.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in terms of network, shell execution, obfuscation, and credential harvesting. However, the metadata risk score is relatively high due to signs of low maintenance and limited author activity, which raises suspicion.

  • Low maintenance effort
  • Limited author activity
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: No shell execution patterns detected, suggesting no direct command execution from the package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows low maintenance effort and an author with limited activity, raising suspicion.

📦 Package Quality Overall: Low (3.2/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

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

Some documentation present

  • Detailed PyPI description (2438 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 16 commits in kbates97/AWS_S3_Explorer_MCP
  • Single author with few commits — possibly a personal or throwaway project

🔬 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

Email domain looks legitimate: gmail.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 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 aws-s3-explorer-mcp 
Create a fully-functional mini-application called 'S3ModelExplorer' using the 'aws-s3-explorer-mcp' package. This application will serve as a bridge between developers and their machine learning models stored in Amazon S3 buckets. The primary goal of S3ModelExplorer is to simplify the process of managing, versioning, and deploying ML models stored in S3 buckets by leveraging the Model Context Protocol (MCP). Here’s a detailed plan on how to build this application:

1. **Setup**: Begin by installing the necessary packages including 'aws-s3-explorer-mcp'. Ensure your AWS credentials are configured properly to interact with S3.
2. **Core Functionality**:
   - Implement a command-line interface (CLI) that allows users to list all models in a specified S3 bucket.
   - Add functionality to download a specific model version from the S3 bucket to the local file system.
   - Include an option to upload a new model version to the S3 bucket, ensuring it adheres to MCP standards.
   - Develop a feature to delete outdated or unused model versions from the S3 bucket.
3. **Advanced Features**:
   - Integrate logging to track all operations performed on the S3 bucket, such as uploads, downloads, and deletions.
   - Incorporate a search function that allows users to find specific model versions based on metadata tags or other attributes.
   - Enable users to compare different versions of the same model to analyze performance improvements or regressions.
4. **User Interface**: While primarily a CLI tool, consider adding a simple web interface using Flask or Django to visualize the models and their versions.
5. **Documentation**: Provide comprehensive documentation for the CLI commands and any web interface, detailing how to install, configure, and use S3ModelExplorer effectively.
6. **Testing**: Write unit tests for each feature to ensure reliability and robustness of the application.
7. **Deployment**: Prepare a Dockerfile to containerize the application, making it easy to deploy and run in various environments.

Throughout the development process, utilize the 'aws-s3-explorer-mcp' package to handle the MCP-related tasks, such as model versioning and context management. This ensures that the application not only serves as a powerful tool for managing ML models but also adheres to best practices in the field of machine learning.

💬 Discussion Feed

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