awslabs.s3-tables-mcp-server

v0.0.25 safe
3.0
Low Risk

An AWS Labs Model Context Protocol (MCP) server for awslabs.s3-tables-mcp-server

🤖 AI Analysis

Final verdict: SAFE

The package appears to be legitimate and secure based on the analysis. There are no indications of malicious activity, and the risks identified are within acceptable limits for a package that interacts with AWS services.

  • Credential risk due to retrieval of AWS credentials from environment variables.
  • Incomplete package functionality as no network calls were detected.
Per-check LLM notes
  • Network: Expected to have network calls related to S3 operations and server functionality, but none detected which may indicate incomplete package functionality or specific deployment conditions.
  • Shell: Shell execution is not expected in a typical server package like this one, unless it contains deployment scripts not used during normal operation.
  • Obfuscation: The observed pattern is a standard method to extend package paths and not indicative of malicious obfuscation.
  • Credentials: The code snippet is retrieving AWS credentials from environment variables, which is a common practice for configuring AWS SDKs but should be handled with caution to prevent unauthorized access.
  • Metadata: The maintainer has only one package, which might indicate a new or less active account, but there are no other red flags.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 14 test file(s) found

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

Some documentation present

  • Documentation URL: "docs" -> https://awslabs.github.io/mcp/servers/s3-tables-mcp-server/
  • Detailed PyPI description (10947 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

  • 65 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 42 unique contributor(s) across 100 commits in awslabs/mcp
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • amespace packages. __path__ = __import__('pkgutil').extend_path(__path__, __name__) # Copyright Amazon.com, In
✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • " region = region_name or os.getenv('AWS_REGION') or 'us-east-1' config = Config(user_agent_extra
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: amazon.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository awslabs/mcp appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Amazon Web Services" 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 awslabs.s3-tables-mcp-server 
Create a fully functional mini-application that leverages the 'awslabs.s3-tables-mcp-server' package to manage and serve tabular data stored in Amazon S3. This application will enable users to upload, download, and query tabular datasets directly from S3 using the Model Context Protocol (MCP). The application should include the following core functionalities:

1. User Authentication: Implement basic user authentication to ensure only authorized users can access the datasets.
2. Dataset Management: Allow users to upload CSV files to their designated S3 bucket, and provide a feature to list all available datasets.
3. Data Querying: Enable users to perform SQL-like queries on the uploaded datasets without downloading them locally.
4. Real-time Updates: Integrate real-time update notifications for datasets, so users are informed when new data is available.
5. Visualization: Provide a simple interface to visualize the queried results using charts or graphs.
6. Error Handling: Ensure robust error handling to gracefully manage any issues during data operations.
7. Documentation: Include comprehensive documentation detailing how to set up and use the application.

To utilize the 'awslabs.s3-tables-mcp-server' package, follow these steps:
- Install the package using pip.
- Configure the MCP server to connect to your S3 bucket where the datasets are stored.
- Use the provided APIs to interact with the datasets, such as uploading, listing, querying, and visualizing data.
- Customize the application to enhance user experience and functionality.

This project aims to demonstrate the power of cloud-based data management and analysis, showcasing how easily one can work with large datasets stored in S3 using Python and AWS services.

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