awslabs.valkey-mcp-server

v1.0.18 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows low risk indicators across all categories, with no detected network calls, shell executions, or credential harvesting attempts. The slight increase in metadata and obfuscation risks is due to the author's newness to PyPI and common code extension techniques.

  • No network calls detected
  • No shell execution patterns
  • Common obfuscation technique used
  • No credential risk detected
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communication.
  • Shell: No shell execution patterns detected, indicating no immediate risk of executing arbitrary commands.
  • Obfuscation: The observed pattern is a common technique for extending package paths and not indicative of malicious obfuscation.
  • Credentials: No patterns indicative of credential harvesting have been detected.
  • Metadata: The author has only one package on PyPI, which may indicate a new or less active account, but no other suspicious activities were flagged.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 24 test file(s) found

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

Some documentation present

  • Documentation URL: "docs" -> https://awslabs.github.io/mcp/servers/valkey-mcp-server/
  • Detailed PyPI description (7750 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

  • 105 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

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

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.valkey-mcp-server 
Create a real-time data visualization dashboard using the 'awslabs.valkey-mcp-server' Python package. This project aims to showcase the power of real-time data processing and visualization by integrating with AWS services. Your task is to develop a mini-application that collects, processes, and visualizes live data streams from various sources such as IoT devices, sensors, or social media feeds.

### Project Overview
The application will consist of two main components:
1. **Data Collector**: This component will simulate or connect to real data sources to collect live data.
2. **Visualization Dashboard**: A web-based interface where users can view the collected data in real-time through interactive charts and graphs.

### Core Features
- **Real-time Data Collection**: Use the 'awslabs.valkey-mcp-server' package to manage the context and model data efficiently, ensuring that the latest data is always available for processing.
- **Data Processing**: Implement basic data filtering and transformation logic to prepare the data for visualization.
- **Interactive Visualization**: Utilize JavaScript libraries like D3.js or Plotly.js to create dynamic, interactive visualizations that update in real-time based on the incoming data.
- **User Interface**: Design a clean, user-friendly web interface using HTML/CSS/JavaScript frameworks like React or Vue.js.
- **Scalability and Performance**: Ensure the application can handle a high volume of data without significant performance degradation.

### Steps to Build the Application
1. **Set Up the Development Environment**:
   - Install necessary Python packages including 'awslabs.valkey-mcp-server'.
   - Set up your AWS credentials for accessing cloud services.
2. **Implement the Data Collector**:
   - Simulate data collection from various sources using mock data or integrate with actual data sources.
   - Configure the 'awslabs.valkey-mcp-server' to manage the data context and ensure seamless data flow.
3. **Develop the Data Processing Logic**:
   - Write scripts to filter, transform, and prepare the collected data for visualization.
4. **Build the Visualization Dashboard**:
   - Create a web interface using a frontend framework.
   - Integrate real-time data streaming capabilities to update the dashboard dynamically.
5. **Test and Deploy**:
   - Conduct thorough testing to ensure all components work together seamlessly.
   - Deploy the application to a cloud environment for easy access and scalability.

### Additional Enhancements
- Add support for multiple data sources and types.
- Implement advanced analytics and machine learning models to provide deeper insights into the data.
- Incorporate user authentication and authorization to control access to sensitive data.
- Provide customization options for the visualization layout and style.

💬 Discussion Feed

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