awslabs.aws-dataprocessing-mcp-server

v0.1.31 safe
3.0
Low Risk

An AWS Labs Model Context Protocol (MCP) server for dataprocessing

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across all assessed categories and does not exhibit signs of malicious activity or supply-chain attacks.

  • No network calls or shell executions detected.
  • Observed obfuscation is common and not indicative of malicious intent.
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external API interactions.
  • Shell: No shell execution patterns detected, indicating the package likely does not execute system commands.
  • Obfuscation: The observed obfuscation pattern is common for extending package paths and does not inherently indicate malicious intent.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The author has only one package, which might indicate a new or less active account but does not necessarily suggest malicious intent.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 34 test file(s) found

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

Some documentation present

  • Documentation URL: "docs" -> https://awslabs.github.io/mcp/servers/aws-dataprocessing-mcp
  • Detailed PyPI description (29747 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

  • 7 type-annotated function signatures (partial)
✦ 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

No credential harvesting patterns detected

✓ 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.aws-dataprocessing-mcp-server 
Create a real-time data processing mini-application using the 'awslabs.aws-dataprocessing-mcp-server' package. This application will serve as a bridge between various data sources and AWS services, enabling real-time analytics and processing capabilities. Your task is to design and implement a system that can ingest streaming data from multiple sources such as IoT devices, social media feeds, or web logs, process it in real-time using AWS services like Kinesis Data Streams, and visualize the processed data through a simple dashboard.

Step-by-Step Guide:
1. Set up the environment by installing necessary packages including 'awslabs.aws-dataprocessing-mcp-server'.
2. Configure the MCP server to connect with AWS services and set up Kinesis Data Streams for ingesting streaming data.
3. Develop a data ingestion module that can connect to different data sources (e.g., MQTT broker for IoT devices, Twitter API for social media feeds).
4. Implement real-time data processing logic using AWS Lambda functions triggered by Kinesis Data Streams.
5. Design a simple web-based dashboard using Flask or Django to display processed data in real-time.
6. Ensure that the application is scalable and can handle high volumes of incoming data streams.
7. Add logging and monitoring capabilities to track the health and performance of the application.

Suggested Features:
- Support for multiple data sources (IoT, social media, web logs)
- Real-time data visualization on the dashboard
- Configurable data processing pipelines
- Automatic scaling based on incoming data volume
- Detailed logging and alerting for operational issues

Utilization of 'awslabs.aws-dataprocessing-mcp-server':
- Use the MCP server to manage model context and facilitate communication between data sources and AWS services.
- Leverage the server's capabilities to efficiently process and route data streams to appropriate AWS services.
- Integrate the MCP server into your data processing pipeline to ensure seamless integration and management of data flow.

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