argus-mcp

v0.8.6 suspicious
5.0
Medium Risk

A central server for MCP servers

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has moderate risks due to network calls and suspicious metadata, though it does not show signs of direct malicious activities like shell execution or credential harvesting.

  • moderate network risk
  • suspicious metadata
Per-check LLM notes
  • Network: The package makes network calls which are not inherently suspicious but should be reviewed for their purpose and destinations.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate threat to secrets or credentials.
  • Metadata: Suspicious non-HTTPS link and lack of GitHub repo suggest potential low-effort or compromised package.

📦 Package Quality Overall: Low (3.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (7887 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

  • 237 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 9.0

Found 6 network call pattern(s)

  • ken}" self._client = httpx.AsyncClient( base_url=self._api_url, headers=hea
  • ry.py. async with httpx.AsyncClient( timeout=timeout, follow_red
  • ting each hop. async with httpx.AsyncClient( timeout=timeout, follow_redirects=False,
  • ) async with httpx.AsyncClient(timeout=30.0) as client: resp = await client.pos
  • client_secret async with httpx.AsyncClient(timeout=timeout) as client: resp = await client.post
  • token_url) async with httpx.AsyncClient(timeout=30.0) as client: resp = await client.pos
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://127.0.0.1:9000
✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author "Daniel Diaz Santiago" 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 argus-mcp 
Create a fully functional mini-application named 'MCP Central Hub' using the Python package 'argus-mcp'. This application will serve as a centralized management tool for multiple MCP (Microservices Control Plane) servers. The primary goal of 'MCP Central Hub' is to provide a unified interface for monitoring, managing, and controlling various aspects of these MCP servers. Here’s a detailed step-by-step guide on what your application should achieve and how it will utilize the 'argus-mcp' package:

1. **Initialization and Configuration**: Start by setting up a configuration file where users can specify details about their MCP servers such as IP addresses, ports, and authentication credentials. Use the 'argus-mcp' package to establish secure connections with each MCP server based on the provided configurations.

2. **Server Monitoring**: Implement real-time monitoring capabilities to track the health status of all connected MCP servers. Display metrics like CPU usage, memory consumption, and network traffic for each server. Utilize 'argus-mcp' functions to fetch live data from each server and present it in a user-friendly dashboard format.

3. **Control Operations**: Provide functionalities to perform common administrative tasks remotely through the hub. Users should be able to restart services, update configurations, and apply patches across multiple MCP servers simultaneously. Leverage 'argus-mcp' commands to execute these operations efficiently.

4. **Alert System**: Set up an alert system that notifies users via email or SMS when any MCP server experiences critical issues such as high load or unexpected downtime. Integrate 'argus-mcp' alerts with external notification services to ensure timely responses.

5. **Logging and Reporting**: Maintain comprehensive logs of all activities performed through the MCP Central Hub and generate periodic reports summarizing server performance and security events. Use 'argus-mcp' logging capabilities to capture essential information and export it in formats like CSV or PDF for analysis.

6. **User Interface**: Develop an intuitive graphical user interface (GUI) using libraries such as Tkinter or PyQt that allows easy navigation and interaction with the hub’s features. Ensure the UI reflects the current state of the MCP servers and provides visual feedback during control operations.

7. **Security Enhancements**: Implement robust security measures including encryption for data transmission, two-factor authentication, and role-based access controls. Securely handle user credentials and ensure that only authorized personnel can manage MCP servers through the hub.

By following these steps and utilizing the 'argus-mcp' package effectively, you will create a powerful yet user-friendly tool for managing MCP servers in a distributed environment.

💬 Discussion Feed

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