🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of potential risks, particularly concerning network calls with unclear API key usage and destination URLs, and suspicious non-HTTPS links. These factors warrant further investigation before deeming it safe.

network risk due to unclear API key usage and destination URLs
metadata risk due to suspicious non-HTTPS links

Per-check LLM notes

Network: The presence of network calls is not uncommon, but the lack of context about the API key usage and destination URL raises some concern.
Shell: No shell execution patterns were detected, which is normal and expected.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, indicating low risk of malicious activity.
Metadata: Suspicious non-HTTPS links indicate potential risk, but lack of other red flags and a single package from the author suggest caution rather than high suspicion.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

api_key else None return httpx.Client(base_url=backend_url, timeout=timeout, headers=headers) de

✓ 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 4.0

Found 2 suspicious link(s) on the package page

Non-HTTPS external link: http://127.0.0.1:8765/mcp
Non-HTTPS external link: http://127.0.0.1:8765/healthz

✓ Git Repository History

Repository SpillwaveSolutions/agent-brain appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Spillwave Solutions" 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 agent-brain-ag-mcp

Create a Python-based mini-application named 'MCP-Toolbox' that leverages the 'agent-brain-ag-mcp' package to serve as a versatile tool for managing and interacting with various computational resources and AI models via the Model Context Protocol (MCP). This application will act as a bridge between different AI models and tools, allowing users to easily invoke these models for tasks such as natural language processing, image generation, and more.

### Core Features:
1. **Resource Management**: Users should be able to add, remove, and manage different computational resources (e.g., GPUs, CPUs) available for AI model execution through a user-friendly interface.
2. **Model Invocation**: Implement functionality to invoke different AI models hosted on the MCP server. This includes specifying parameters for each model invocation and receiving results back from the server.
3. **Task Execution**: Allow users to submit tasks to the MCP server for processing. These tasks could range from simple text analysis to complex image generation requests.
4. **Result Visualization**: Provide visual outputs of the results returned by the AI models, such as graphs, images, or detailed text analyses.
5. **Custom Prompt Generation**: Enable users to create custom prompts for model interactions, which can then be saved and reused for future tasks.

### How to Utilize 'agent-brain-ag-mcp':
- Use the package to establish a connection to the MCP server, enabling the application to communicate with and manage resources and models hosted there.
- Leverage the package’s tools for invoking models and executing tasks, ensuring that all interactions adhere to the MCP protocol.
- Implement error handling and logging mechanisms using the package’s capabilities to ensure smooth operation and easy debugging.

### Development Steps:
1. Set up the development environment with Python and install the 'agent-brain-ag-mcp' package.
2. Design the user interface for resource management and task submission.
3. Develop the backend logic for connecting to the MCP server, managing resources, and invoking models.
4. Integrate result visualization components into the application.
5. Test the application thoroughly, ensuring that all core features work as expected.
6. Document the application’s usage and include examples for common tasks.
7. Deploy the application and make it accessible to other developers and end-users.