ansible-tower-mcp

v1.31.0 suspicious
4.0
Medium Risk

Ansible Tower MCP Server for Agentic AI!

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of suspicious behavior, particularly concerning metadata and network interactions, though direct threats like credential harvesting or obfuscation are not evident.

  • Suspicious metadata with missing maintainer information
  • Potential unsecured network communication
Per-check LLM notes
  • Network: The network calls are likely for making HTTP requests to an API endpoint, possibly for configuration management purposes.
  • Shell: The shell execution pattern indicates interaction with the Git command-line tool, which is common for source control operations but should be reviewed for context to ensure it aligns with legitimate package functionality.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: Suspicious non-HTTPS link and lack of maintainer information suggest potential risks.

📦 Package Quality Overall: Medium (5.2/10)

✦ High Test Suite 9.0

Test suite present — 8 test file(s) found

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

Some documentation present

  • Detailed PyPI description (13061 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 109 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 3.0

Found 2 network call pattern(s)

  • ecret self._session = requests.Session() self._session.verify = verify self.proxies
  • ains?", ] async with httpx.AsyncClient(timeout=10000.0) as client: for q in questions:
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • h): try: result = subprocess.run( ["git", "ls-files", "--cached", "--others", "--
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://ansible-tower-mcp-mcp:8000/mcp
✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 ansible-tower-mcp 
Develop a mini-application called 'AgenticAIController' that leverages the Ansible Tower MCP Server for orchestrating tasks in an AI-driven environment. This application will serve as a bridge between AI agents and the infrastructure managed by Ansible Tower. The primary goal is to enable AI-driven decision-making to trigger specific playbooks or tasks within Ansible Tower.

### Features:
1. **User Interface**: A simple web-based interface where users can input task requests or queries that an AI model can understand.
2. **AI Integration**: Utilize an external API or a local model (e.g., using transformers or similar libraries) to interpret user inputs and decide on the appropriate playbook or task to execute.
3. **Ansible Tower Interaction**: Use the `ansible-tower-mcp` package to communicate with Ansible Tower, triggering the selected playbook or task based on the AI's decision.
4. **Logging & Monitoring**: Implement logging of all interactions and a monitoring dashboard to track the status of triggered tasks/playbooks.
5. **Security Measures**: Ensure secure communication with both the AI model and Ansible Tower, possibly using OAuth tokens or other authentication methods supported by Ansible Tower.
6. **Customization Options**: Allow users to customize which playbooks/tasks are available for execution via the UI, and how the AI interprets certain commands.

### Steps to Develop:
1. **Setup Environment**: Install necessary packages including Flask for the web interface, transformers or equivalent for AI model integration, and `ansible-tower-mcp` for interacting with Ansible Tower.
2. **Design UI**: Create a clean, user-friendly interface allowing users to interact with the system and see real-time updates on task statuses.
3. **Integrate AI Model**: Connect your chosen AI model to interpret user inputs. This could involve training a model if specific needs arise, or using pre-trained models available through APIs.
4. **Implement Ansible Tower Communication**: Utilize the `ansible-tower-mcp` package to establish a connection with Ansible Tower. Write functions to handle the triggering of specific playbooks based on the AI's decisions.
5. **Develop Logging & Monitoring**: Set up logging for all interactions and develop a dashboard that displays the current status of all triggered tasks.
6. **Security Enhancements**: Implement security measures such as token-based authentication to ensure only authorized requests are processed.
7. **Testing & Deployment**: Thoroughly test the application to ensure it works as expected under various conditions. Deploy the application in a secure environment accessible to users.

By completing this project, you will create a powerful tool that integrates AI-driven decision-making with automated infrastructure management, showcasing the potential of combining advanced technologies for efficient task orchestration.

💬 Discussion Feed

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