awslabs.amazon-bedrock-agentcore-mcp-server

v0.1.1 suspicious
6.0
Medium Risk

Model Context Protocol (MCP) server for Amazon Bedrock AgentCore services

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits behaviors that raise concerns, particularly the high credential risk score due to attempts to read sensitive files. However, the low scores in other categories do not strongly indicate malicious intent.

  • High credential risk score
  • Attempts to read sensitive files
Per-check LLM notes
  • Network: The network call patterns suggest the package may perform HTTP requests, which could be legitimate if it interacts with AWS services as indicated by its name.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The obfuscation pattern detected is common and often used to extend package paths dynamically; it does not indicate malicious activity.
  • Credentials: The code attempts to read sensitive files like '/etc/passwd' and '/etc/hosts', which could be indicative of credential harvesting or other malicious activities.
  • Metadata: The author has only one package on PyPI, which may indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 59 test file(s) found

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

Some documentation present

  • Documentation URL: "docs" -> https://awslabs.github.io/mcp/servers/amazon-bedrock-agentco
  • Detailed PyPI description (11847 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
✦ 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 3.0

Found 2 network call pattern(s)

  • ng utilities.""" @patch('urllib.request.urlopen') def test_get_success(self, mock_urlopen):
  • = 'Test content' @patch('urllib.request.urlopen') def test_get_handles_encoding_errors(self, moc
⚠ 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 5 credential access pattern(s)

  • yncMock, MagicMock REGION = os.getenv('AWS_REGION', 'us-east-1') # ----------------------------------
  • validate_urls('file:///etc/passwd') with pytest.raises(URLValidationError):
  • 'File upload') # Use /etc/hosts — exists on both macOS and Linux result = await int
  • file_ref, paths=['/etc/hosts'], ) assert 'Uploaded' in result asyn
  • script>', 'file:///etc/passwd', ], ids=['javascript', 'data', 'file'],
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: users.noreply.github.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, Primo Mu" 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.amazon-bedrock-agentcore-mcp-server 
Create a Python-based mini-application that leverages the 'awslabs.amazon-bedrock-agentcore-mcp-server' package to manage and interact with Amazon Bedrock AgentCore services. Your application should serve as a simple yet powerful tool for developers looking to integrate context-aware AI into their applications. Here’s a step-by-step guide on how to develop this application:

1. **Setup Environment**: Ensure your development environment includes Python and the necessary AWS SDK packages. Install the 'awslabs.amazon-bedrock-agentcore-mcp-server' package.
2. **Design Application Structure**: Plan the structure of your application, including modules for initialization, context management, and interaction with the MCP server.
3. **Context Management**: Implement functionality to manage contexts within the MCP server. This includes creating, updating, and deleting contexts.
4. **Interaction with MCP Server**: Use the 'awslabs.amazon-bedrock-agentcore-mcp-server' package to establish a connection to the MCP server and perform operations such as sending requests, receiving responses, and handling errors.
5. **User Interface**: Develop a simple command-line interface (CLI) for users to interact with your application. Provide options to create, update, delete contexts, and send queries to the MCP server.
6. **Security and Compliance**: Ensure that all interactions with the MCP server comply with AWS security standards. Implement proper authentication and authorization mechanisms.
7. **Testing and Validation**: Thoroughly test your application to ensure it works as expected under various conditions. Validate its ability to handle different types of inputs and scenarios.
8. **Documentation**: Write comprehensive documentation for your application, detailing installation, configuration, usage, and any known limitations.

Suggested Features:
- Real-time context updates
- Support for multiple concurrent connections
- Detailed logging and error handling
- Integration with popular cloud monitoring tools
- Flexible configuration options for customizing behavior

By following these steps and implementing the suggested features, you will create a valuable tool that simplifies the process of integrating Amazon Bedrock AgentCore services into applications, making it easier for developers to leverage advanced AI capabilities.

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