🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some potential risks, primarily due to the presence of suspicious metadata and a potential credential harvesting mechanism in test code, though it seems intended for testing.

Suspicious non-HTTPS link and missing repository in metadata.
Potential risk of credential harvesting in test code.

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communications.
Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
Obfuscation: No obfuscation patterns detected.
Credentials: Potential risk of credential harvesting observed in test code but appears to be for testing purposes rather than actual credential theft.
Metadata: Suspicious non-HTTPS link and missing repository suggest potential risk.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 10.0

Found 4 credential access pattern(s)

out = await adapter.run(path="/etc/hosts") assert out == "<file body>" assert runner.calls =
s == [("read_file", {"path": "/etc/hosts"})] @pytest.mark.asyncio async def test_adapter_strips_se
t = await read_file.run(path="/etc/hosts") assert result == "<body>" # The runner sees the b
s == [("read_file", {"path": "/etc/hosts"})] @pytest.mark.asyncio async def test_close_propagates_

✓ 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://internal:8080/mcp

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "The AgentForge Authors" 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 agentforge-mcp

Create a Python-based mini-application named 'ContextualQueryBot' that leverages the 'agentforge-mcp' package to facilitate intelligent querying and context management. This application will serve as a personal assistant that can answer questions based on a provided context, ensuring that the answers are relevant and accurate within the scope of the given information.

### Features:
1. **Context Integration:** Allow users to upload or input a document or set of documents as the context from which queries will be answered. This could be in various formats such as .txt, .pdf, or .docx.
2. **Query Handling:** Implement a user-friendly interface where users can type their questions related to the uploaded context. The application should then return answers based on the context.
3. **Dynamic Updates:** If the context changes, the application should allow for dynamic updates to ensure that the latest information is used for answering queries.
4. **History Management:** Keep track of past queries and their corresponding answers for reference and learning purposes.
5. **User Authentication:** To ensure privacy and personalization, implement a simple authentication system allowing users to save and manage their contexts and histories.
6. **Feedback Loop:** Provide users with the ability to rate the accuracy and relevance of the answers, which can help improve future responses.

### Utilizing 'agentforge-mcp':
- **Model Context Protocol (MCP):** Use the MCP framework provided by 'agentforge-mcp' to handle the integration of different models with varying contexts. This ensures that the application can adapt its responses based on the specific context provided by the user.
- **Context Management:** Leverage the context management capabilities of 'agentforge-mcp' to dynamically adjust the context based on user inputs or document uploads, ensuring that the model always has the most relevant information available.
- **Integration with AI Models:** Integrate with various AI models supported by 'agentforge-mcp' to enhance the intelligence and versatility of the responses provided by the application.
- **Scalability:** Design the application to be scalable, allowing it to handle multiple contexts and users efficiently.

Your task is to design and implement this application using Python, ensuring that it is user-friendly and efficient. Document your process and any challenges faced during development, focusing particularly on how 'agentforge-mcp' enhances the functionality of your application.