awslabs.prometheus-mcp-server

v0.2.17 safe
2.0
Low Risk

MCP server for interacting with AWS Managed Prometheus

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal signs of potential risk, with low scores across all assessed categories. It appears to be a legitimate tool for interfacing with AWS Managed Prometheus.

  • Low network risk
  • No shell execution detected
  • Standard credential handling practices
Per-check LLM notes
  • Network: The use of requests.Session() suggests the package is making network calls, likely for legitimate purposes such as fetching metrics or communicating with AWS services.
  • Shell: No shell execution patterns detected, indicating no risk from this aspect.
  • Obfuscation: The observed pattern is a common technique for extending package paths and does not indicate malicious obfuscation.
  • Credentials: The code snippet appears to be a standard way of fetching AWS region from environment variables or default settings, indicating legitimate credential handling rather than malicious harvesting.
  • Metadata: The maintainer has only one package, suggesting a new or less active account, but no other red flags are present.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 16 test file(s) found

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

Some documentation present

  • Documentation URL: "docs" -> https://awslabs.github.io/mcp/servers/prometheus-mcp-server/
  • Detailed PyPI description (7124 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 score 1.5

Found 1 network call pattern(s)

  • request with requests.Session() as req_session: logger.debug(
⚠ 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)

  • t region = region_name or os.getenv('AWS_REGION') or DEFAULT_AWS_REGION # Configure custom user
  • aws_region = region or os.getenv('AWS_REGION') or DEFAULT_AWS_REGION aws_profile = profile
✓ 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.prometheus-mcp-server 
Create a monitoring dashboard application using Python that integrates with AWS Managed Prometheus via the 'awslabs.prometheus-mcp-server' package. This application will allow users to visualize real-time metrics from their AWS resources, providing insights into system performance and health. Here’s a step-by-step guide on how to develop this application:

1. **Setup Project Environment**: Initialize a new Python project and install the required packages including 'awslabs.prometheus-mcp-server'. Ensure you have access to AWS Managed Prometheus.
2. **Authentication & Configuration**: Configure your application to authenticate with AWS services securely. Use environment variables or a configuration file to store sensitive information like API keys and access tokens.
3. **Fetch Metrics**: Utilize the 'awslabs.prometheus-mcp-server' package to query metrics from AWS Managed Prometheus. Implement functions to fetch different types of metrics based on user input or predefined categories.
4. **Data Visualization**: Integrate a data visualization library such as Plotly or Matplotlib to display the fetched metrics in real-time. Design interactive charts and graphs that update automatically as new data comes in.
5. **Dashboard Interface**: Develop a simple web interface using Flask or Django where users can select which metrics they want to view and customize the display settings. Ensure the UI is user-friendly and responsive.
6. **Alerting System**: Implement an alerting feature that triggers notifications when certain thresholds are exceeded. Use AWS SNS or another notification service to send alerts via email, SMS, or Slack.
7. **Documentation & Testing**: Write comprehensive documentation explaining how to set up and use the application. Perform thorough testing to ensure reliability and accuracy of the metrics displayed.
8. **Deployment**: Deploy your application on a cloud platform like AWS Elastic Beanstalk or Heroku for easy scalability and maintenance.

Suggested Features:
- Real-time updates for metric visualizations.
- Ability to filter metrics by time range, resource type, or specific tags.
- Customizable alert thresholds and notification preferences.
- Detailed logs and error handling mechanisms.

💬 Discussion Feed

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