ai-task-board-mcp

v0.2.0 suspicious
5.0
Medium Risk

MCP server exposing the AI Task Board to any MCP-capable AI agent

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network, shell, obfuscation, and credential handling but raises concerns due to its new upload status and limited maintainer history on PyPI. Additionally, the presence of a non-HTTPS link increases suspicion.

  • New package with limited maintainer history
  • Non-HTTPS link present
  • Low risks in other categories
Per-check LLM notes
  • Network: The detected network calls suggest the package is designed to communicate with an API endpoint, which is common for packages that interact with remote services.
  • Shell: No shell execution patterns were detected, indicating there is no evidence of direct system command execution within the package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package is newly uploaded and the maintainer has a limited history with PyPI, raising some suspicion. The presence of a non-HTTPS link also adds to the concern.

📦 Package Quality Overall: Low (4.2/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://board.filbert.games/docs
  • Detailed PyPI description (2956 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

  • 22 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)

  • config self._client = httpx.Client( base_url=f"{config.base_url}/api/v1",
  • be told who/how long with httpx.Client(base_url=f"{BASE}/api/v1", headers={"A
✓ 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://127.0.0.1:8077
✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Package is very new: uploaded 2 day(s) ago
  • Author "Danylo Lahodniuk" 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-task-board-mcp 
Create a Python-based mini-application called 'TaskMaster' that leverages the 'ai-task-board-mcp' package to manage and execute tasks for various AI agents. The application should serve as a central hub where different AI agents can post their task requests and receive instructions on which tasks to perform next. Here’s a detailed breakdown of the project requirements and steps:

1. **Setup**: Begin by installing the 'ai-task-board-mcp' package and setting up a basic Flask web server.
2. **User Interface**: Develop a simple UI using HTML/CSS/JavaScript to allow users to input task descriptions and assign them to specific AI agents.
3. **Task Management**: Implement functionality within the Flask server to accept task postings from AI agents via the 'ai-task-board-mcp' API and store these tasks in a database.
4. **Task Assignment**: Design a system that automatically assigns tasks to available AI agents based on predefined criteria (e.g., agent capabilities, task urgency).
5. **Agent Communication**: Use the 'ai-task-board-mcp' package to communicate task assignments back to the AI agents and receive updates on task completion status.
6. **Status Tracking**: Integrate a feature that allows users to track the progress of tasks in real-time through the UI.
7. **Reporting**: Create a reporting module that generates periodic reports summarizing completed tasks, ongoing tasks, and any issues encountered during task execution.
8. **Security Measures**: Ensure that the application includes basic security measures such as user authentication and data encryption to protect task information.

Suggested Features:
- Integration with popular AI frameworks like TensorFlow or PyTorch for specific task types.
- Support for multiple task types (classification, regression, clustering, etc.)
- Ability to prioritize tasks based on user-defined rules.
- Real-time notifications for task assignment and completion.
- Detailed logging and error handling for robustness.

The 'ai-task-board-mcp' package is crucial for enabling communication between the Flask server and AI agents, facilitating seamless task posting, assignment, and status updates. Your goal is to create a user-friendly, efficient, and secure platform that streamlines AI task management.