astro-airflow-mcp

v0.8.2 safe
4.0
Medium Risk

A FastMCP server for Airflow integration that can run standalone or as an Airflow 2/3 plugin

🤖 AI Analysis

Final verdict: SAFE

The package exhibits minimal risks across all categories with no signs of malicious behavior. It has slightly concerning metadata quality and maintenance status, but these alone do not indicate a supply-chain attack.

  • Low network and shell execution risks.
  • No obfuscation or credential harvesting detected.
  • Metadata quality and maintenance could be improved.
Per-check LLM notes
  • Network: Network calls are common in packages that interact with external services or APIs.
  • Shell: Shell executions are likely for running CLI tools or scripts necessary for the package's functionality.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows low maintenance and metadata quality, but lacks clear indicators of malicious intent.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 9 test file(s) found

  • Test runner config found: conftest.py
  • 9 test file(s) detected (e.g. __init__.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (29627 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

  • 320 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • try: with httpx.Client(timeout=self._timeout, verify=self._verify) as client:
  • try: with httpx.Client(timeout=10.0, verify=verify) as client: resp
  • v is not None} with httpx.Client(timeout=30.0, verify=self._verify) as client: re
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • ss(Exception): proc = subprocess.Popen( # nosec B603 - no untrusted input, script and args are har
  • stro_path() result = subprocess.run( # nosec B603 - astro CLI path is validated via shutil.whic
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with astro-airflow-mcp 
Develop a mini-application that integrates Astro-Airflow-MCP to create a streamlined data pipeline monitoring system. This application will serve as a FastMCP server for Airflow, allowing users to monitor and manage their data pipelines more effectively. Here’s a step-by-step guide on how to approach this project:

1. **Project Setup**: Begin by setting up your Python environment with the necessary packages including `astro-airflow-mcp`, `airflow`, and any other dependencies required for a robust FastMCP server.

2. **Integration of Astro-Airflow-MCP**: Utilize the `astro-airflow-mcp` package to set up a FastMCP server that can either run independently or be integrated as a plugin into Airflow 2 or 3. Ensure that the server is capable of communicating seamlessly with Airflow's scheduler and worker nodes.

3. **Feature Implementation**:
   - **Real-time Monitoring**: Implement real-time monitoring capabilities where users can track the status of their data pipelines in real-time. This includes visual representations of pipeline progress, success rates, and failure points.
   - **Alert System**: Develop an alert system that notifies users via email or SMS when a pipeline fails or when it reaches certain performance thresholds.
   - **Custom Dashboards**: Allow users to customize dashboards based on their specific needs, providing them with the ability to focus on key metrics and KPIs relevant to their projects.
   - **Historical Data Analysis**: Enable historical data analysis features that allow users to review past performance and identify trends or issues over time.

4. **User Interface**: Design a user-friendly interface that simplifies interaction with the FastMCP server. Consider using frameworks like Flask or Django for backend logic and React or Angular for frontend development.

5. **Testing and Deployment**: Conduct thorough testing to ensure reliability and efficiency. Once tested, deploy the application in a production environment, ensuring it meets all performance and security standards.

6. **Documentation and Support**: Provide comprehensive documentation for both end-users and developers. Include tutorials, FAQs, and support channels to assist users in getting the most out of the application.

By following these steps, you'll create a powerful tool for managing and monitoring data pipelines, leveraging the capabilities of `astro-airflow-mcp` to enhance productivity and efficiency in data processing workflows.

💬 Discussion Feed

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