awslabs.aws-api-mcp-server

v1.3.42 safe
4.0
Medium Risk

Model Context Protocol (MCP) server for interacting with AWS

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across most categories, with only moderate credential handling concerns. These risks are mitigated by the package's legitimate purpose and affiliation with AWS.

  • moderate credential handling risk
  • legitimate use case for interacting with AWS services
Per-check LLM notes
  • Network: Network POST calls are expected if the package interacts with AWS services via APIs.
  • Shell: No shell execution patterns detected, indicating low risk for direct system command execution.
  • Obfuscation: The detected pattern is a common method for extending Python package paths and does not indicate malicious obfuscation.
  • Credentials: The code retrieves environment variables related to AWS credentials and settings, which could be a legitimate practice but also poses a risk if not handled securely.
  • Metadata: The presence of a non-HTTPS link and a single package from an author associated with Amazon raises some concerns but does not strongly indicate malicious intent.

📦 Package Quality Overall: Medium (7.0/10)

✦ 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. fixtures.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "docs" -> https://awslabs.github.io/mcp/servers/aws-api-mcp-server/
  • Detailed PyPI description (40427 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 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 126 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 score 1.5

Found 1 network call pattern(s)

  • 'POST'}, ) session = requests.Session() adapter = HTTPAdapter(max_retries=retry_strategy)
⚠ 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 10.0

Found 6 credential access pattern(s)

  • a default.""" transport = os.getenv('AWS_API_MCP_TRANSPORT', 'stdio') if transport not in ['stdio
  • ') AWS_API_MCP_PROFILE_NAME = os.getenv('AWS_API_MCP_PROFILE_NAME') AWS_REGION = os.getenv('AWS_REGION')
  • P_PROFILE_NAME') AWS_REGION = os.getenv('AWS_REGION') DEFAULT_REGION = get_region(AWS_API_MCP_PROFILE_NAM
  • t_transport_from_env() HOST = os.getenv('AWS_API_MCP_HOST', '127.0.0.1') PORT = int(os.getenv('AWS_API_MC
  • OST', '127.0.0.1') PORT = int(os.getenv('AWS_API_MCP_PORT', 8000)) ALLOWED_HOSTS = os.getenv('AWS_API_MCP
  • PORT', 8000)) ALLOWED_HOSTS = os.getenv('AWS_API_MCP_ALLOWED_HOSTS', HOST) ALLOWED_ORIGINS = os.getenv('A
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: users.noreply.github.com>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://127.0.0.1:8000/mcp
✓ 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-api-mcp-server 
Create a fully-functional mini-application called 'AWS MCP Manager' using the Python package 'awslabs.aws-api-mcp-server'. This application will serve as a bridge between local machine models and AWS services, enabling users to manage their model context more efficiently. Here are the steps and features you need to implement:

1. **Setup**: Initialize your environment by installing the necessary packages including 'awslabs.aws-api-mcp-server'. Ensure you have the required AWS credentials configured.
2. **Authentication**: Implement a secure authentication mechanism to verify user access to AWS services.
3. **Model Management**: Develop functionalities to upload, download, and manage models stored in AWS S3. Use 'awslabs.aws-api-mcp-server' to handle the interaction protocols with AWS services.
4. **Contextual Interaction**: Enable users to send model context requests to AWS and receive responses back. This includes setting up the MCP server to interpret these requests and return appropriate responses based on the AWS service interaction.
5. **User Interface**: Design a simple yet intuitive command-line interface (CLI) for users to interact with the application. Commands should include options for uploading models, downloading models, listing available models, and sending model context requests.
6. **Logging and Monitoring**: Integrate logging to track user interactions and model operations. Optionally, add monitoring capabilities to keep an eye on the performance of the MCP server.
7. **Documentation**: Provide clear documentation detailing how to install the application, set up AWS credentials, and use the CLI commands effectively.

Suggested Features:
- Support for multiple AWS regions.
- Automatic cleanup of unused models.
- Detailed error messages for failed operations.
- Ability to schedule regular backups of models.

The 'awslabs.aws-api-mcp-server' package will be utilized extensively for handling the communication protocols between the local machine and AWS services, ensuring seamless integration and efficient management of model contexts.

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