awslabs.stepfunctions-tool-mcp-server

v0.1.24 safe
3.0
Low Risk

An AWS Labs Model Context Protocol (MCP) server for AWS Step Functions

🤖 AI Analysis

Final verdict: SAFE

The package appears safe based on the analysis. It does not engage in any potentially harmful activities such as making unauthorized network calls or executing shell commands.

  • No network risk detected
  • No shell execution detected
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands.
  • Obfuscation: The observed pattern is a common technique to extend the search path for packages and does not inherently indicate malicious activity.
  • Credentials: The code accesses environment variables for AWS credentials which could be a legitimate practice but also raises concerns about insecure handling of secrets.
  • Metadata: The author has only one package, which may indicate a new or less active account, but no other suspicious flags were found.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 14 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://awslabs.github.io/mcp/servers/stepfunctions-tool-mcp
  • Detailed PyPI description (11282 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

  • 10 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 score 5.0

Found 2 credential access pattern(s)

  • se """ return os.environ.get('AWS_REGION', 'us-east-1') @staticmethod def get_aws_pro
  • se """ return os.environ.get('AWS_PROFILE') @staticmethod def create_boto3_client(ser
✓ 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" 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.stepfunctions-tool-mcp-server 
Create a mini-application named 'ModelExecutionOrchestrator' that leverages the 'awslabs.stepfunctions-tool-mcp-server' package to manage the execution of machine learning models hosted on AWS. This application will serve as a bridge between users who want to run inference on their models and the AWS infrastructure, ensuring that all requests are processed efficiently and securely.

The core functionality of the application includes:
1. Accepting model inference requests from users through a REST API endpoint.
2. Validating the request payload against predefined schemas to ensure it contains all necessary information such as model name, input data, etc.
3. Using AWS Step Functions to orchestrate the execution of the requested model. This involves invoking the MCP server to handle the context and lifecycle management of the model execution.
4. Handling errors gracefully and providing informative responses to the user if something goes wrong during the execution process.
5. Storing the results of each model execution in a database for future reference or auditing purposes.

Suggested additional features include:
- Implementing rate limiting on the REST API to prevent abuse and ensure fair usage.
- Providing detailed logs for each execution, stored in AWS CloudWatch or similar logging services.
- Allowing users to define custom timeouts for model execution to accommodate different use cases.
- Integrating with AWS IAM for authentication and authorization, ensuring only authorized users can execute models.
- Offering a dashboard to visualize execution statistics, such as average response time, number of successful executions, etc.

To utilize the 'awslabs.stepfunctions-tool-mcp-server' package, you'll need to set up an MCP server instance that your application can communicate with. This involves configuring the server to understand the context of the models it will be managing, including how to start, stop, and monitor them. Your application should then be able to send commands to this server via its API, instructing it to perform specific actions related to model execution. Additionally, consider how the package's capabilities can be extended or customized to better fit the needs of your application, such as adding support for different types of models or integrating with other AWS services.

💬 Discussion Feed

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