🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to potential misuse of shell commands and unexplained network calls, although it shows no signs of obfuscation or credential mishandling.

moderate network risk due to localhost calls
high shell risk from potentially invasive command execution

Per-check LLM notes

Network: The network calls to localhost suggest internal health checks, which are common but could indicate unexpected behavior if not documented.
Shell: Executing shell commands like 'ps aux' might be used for monitoring purposes but can also be indicative of more invasive operations, increasing the risk.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, suggesting secure handling of sensitive information.

📦 Package Quality Overall: Low (4.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

1 test file(s) detected (e.g. test_server.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (4363 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

17 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 6.0

Limited contributor diversity

2 unique contributor(s) across 44 commits in CSOAI-ORG/ai-ops-mcp
Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

t = time.time() req = urllib.request.urlopen(url, timeout=5) latency = round((time.time()
try: resp = urllib.request.urlopen("http://localhost:8000/health", timeout=2)

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

err try: result = subprocess.run(["ps", "aux"], capture_output=True, text=True, timeout=5)

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: meok.ai>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ 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 ai-ops-mcp

Create a comprehensive system monitoring tool using the 'ai-ops-mcp' package. This tool will serve as a one-stop solution for administrators to monitor the health of their systems, check services, and perform security scans. Here’s a detailed outline of the steps and features your application should include:

1. **Setup**: Begin by installing the 'ai-ops-mcp' package. Ensure that you have Python installed on your machine, and then use pip to install the package.
2. **System Health Check**: Implement a feature that periodically checks the overall health of the system. Use the 'ai-ops-mcp' package to gather information about CPU usage, memory consumption, disk space, network activity, etc. Display this information in a user-friendly format.
3. **Service Monitoring**: Develop a component that monitors the status of critical services running on the system. Utilize 'ai-ops-mcp' to detect whether these services are up and running, and alert the user if any service goes down.
4. **Security Scan**: Integrate a security scanning feature that uses 'ai-ops-mcp' to identify potential vulnerabilities in the system. This could include checking for outdated software, weak passwords, or other common security issues.
5. **User Interface**: Design a simple yet effective GUI or CLI interface through which users can interact with the tool. Ensure that it is easy to navigate and provides clear insights into the system’s state.
6. **Notifications**: Set up a notification system that alerts users via email, SMS, or push notifications when there are critical issues detected by the tool.
7. **Logging and Reporting**: Incorporate logging functionality to keep track of all activities performed by the tool. Also, generate detailed reports that summarize the system’s performance over time.
8. **Customization**: Allow users to customize the frequency of checks, the types of services monitored, and the thresholds for alerts.

By following these steps, you'll create a powerful system monitoring tool that leverages the capabilities of the 'ai-ops-mcp' package to ensure the health, stability, and security of IT environments.