awslabs.aws-bedrock-custom-model-import-mcp-server

v0.0.18 safe
2.0
Low Risk

An AWS Labs Model Context Protocol (MCP) server for Bedrock Custom Model Import

🤖 AI Analysis

Final verdict: SAFE

The package appears to be safe with no detected network calls, shell executions, or credential harvesting activities. The low risk score suggests it is unlikely to be involved in a supply-chain attack.

  • No network calls detected.
  • No shell execution patterns found.
Per-check LLM notes
  • Network: No network calls detected, which is unusual for AWS-related packages but may be due to external configuration expectations.
  • Shell: No shell execution patterns detected, indicating the package does not execute commands on the host system.
  • Obfuscation: The observed obfuscation pattern is common and typically used for extending package paths rather than malicious purposes.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The author has only one package on PyPI, 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 — 13 test file(s) found

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

Some documentation present

  • Documentation URL: "docs" -> https://awslabs.github.io/mcp/servers/aws-bedrock-custom-mod
  • Detailed PyPI description (13490 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

  • 15 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: 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-bedrock-custom-model-import-mcp-server 
Create a mini-application that facilitates the deployment and management of custom models on AWS Bedrock using the 'awslabs.aws-bedrock-custom-model-import-mcp-server' package. This application will serve as a bridge between local model files and AWS Bedrock, allowing users to upload, manage, and interact with their custom models more efficiently.

### Core Functionality:
1. **Model Upload**: Implement a feature where users can select a local machine learning model file (e.g., TensorFlow, PyTorch) and upload it to the application. The application will then use the 'awslabs.aws-bedrock-custom-model-import-mcp-server' package to prepare and send the model to AWS Bedrock.
2. **Model Management**: Once uploaded, users should be able to view a list of all their models hosted on AWS Bedrock through the application. They should also have options to delete models or update existing ones.
3. **Model Interaction**: Users should be able to invoke the deployed models directly from the application, receiving real-time responses or predictions based on input data they provide.

### Suggested Features:
- **User Authentication**: Integrate basic user authentication to ensure only authorized users can access and manage their models.
- **Model Versioning**: Allow users to keep track of different versions of the same model, making it easier to revert to previous versions if needed.
- **Performance Metrics**: Provide users with performance metrics (e.g., latency, accuracy) for their deployed models.
- **Documentation and Support**: Include comprehensive documentation and support within the application to guide users through the process of uploading and managing their models.

### Utilization of 'awslabs.aws-bedrock-custom-model-import-mcp-server':
- Use the package to handle the conversion and preparation of local model files into formats compatible with AWS Bedrock.
- Leverage the MCP server provided by the package to facilitate communication between your application and AWS Bedrock, ensuring seamless model import and management.
- Integrate the package's functionalities to monitor the status of model uploads and updates, providing real-time feedback to users about the progress of these operations.

This project aims to streamline the process of deploying custom models on AWS Bedrock, making it accessible and efficient for developers and data scientists.

💬 Discussion Feed

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