AI Analysis
The package shows low individual risk factors but has a concerning metadata risk due to the unverified maintainer and missing repository. This combination raises suspicion but does not conclusively indicate malicious intent.
- Low risk in network, shell, obfuscation, and credential aspects.
- High metadata risk due to unknown maintainer and missing repository.
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, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The repository is not found and the maintainer seems new with limited history, raising concerns.
Package Quality Overall: Low (4.4/10)
Test suite present — 3 test file(s) found
Test runner config found: conftest.pyTest runner config found: pyproject.toml3 test file(s) detected (e.g. conftest.py)
Some documentation present
Detailed PyPI description (2132 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
11 type-annotated function signatures detected in source
Could not retrieve contributor data from GitHub
GitHub API error: 404
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
No author email provided
All external links appear legitimate
Repository not found (deleted or private)
Repository not found (deleted or private)
2 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor "Agent Identity Registry Foundation" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a fully functional mini-application that serves as a Model Context Protocol (MCP) server using the 'air-mcp-server' Python package. This application will act as a bridge between various AI models and the Agent Identity Registry (AIR), enabling seamless communication and context management for these models. Your task is to develop a command-line interface (CLI) tool that allows users to register, manage, and interact with different AI models within the AIR ecosystem. ### Core Features: - **Model Registration:** Users should be able to register new models with the MCP server. Each model should have a unique identifier and metadata such as version, description, and supported operations. - **Context Management:** Implement functionality to manage contexts for each model. Contexts represent the state of a model during its operation, including input data, intermediate results, and output configurations. - **Interaction Interface:** Provide a simple CLI where users can query the status of registered models, update their contexts, and retrieve information about ongoing operations. ### Advanced Features (Optional): - **Web Dashboard Integration:** Extend your application to include a basic web dashboard that visualizes the status of registered models and their contexts. - **Security Enhancements:** Incorporate basic security measures like user authentication and authorization to control access to the MCP server. - **Logging and Monitoring:** Integrate logging capabilities to track all interactions with the MCP server, and provide monitoring tools to analyze the performance and usage patterns of the models. ### Utilization of 'air-mcp-server': - Use the 'air-mcp-server' package to handle the underlying communication protocols and interactions with the AIR registry. Ensure that your application leverages the package's APIs to facilitate registration, context management, and interaction functionalities efficiently. - Explore the documentation of 'air-mcp-server' to understand its capabilities and limitations, and design your application architecture accordingly. Your final submission should include a well-documented codebase, clear instructions on setting up and running the application, and a demonstration video showcasing its key features.
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