🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits medium risk due to its potential for executing shell commands and making network requests, alongside metadata suggesting it may be newly created with limited maintainer history.

Shell risk of 7/10
Network risk of 3/10
Metadata risk of 6/10

Per-check LLM notes

Network: The use of AsyncClient suggests the package may be making network requests to an external service, which is not inherently malicious but should be reviewed for legitimacy.
Shell: The execution of shell commands can pose significant risks if misused, indicating potential for unauthorized actions or data exfiltration.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of being newly created with limited maintainer history and an incomplete author profile, raising concerns about its legitimacy.

📦 Package Quality Overall: Medium (5.0/10)

✦ High Test Suite 9.0

Test suite present — 19 test file(s) found

Test runner config found: conftest.py
Test runner config found: conftest.py
Test runner config found: conftest.py
19 test file(s) detected (e.g. conftest.py)

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://github.com/dannybombastic/mcp-standard-ai#readme
Detailed PyPI description (4986 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

158 type-annotated function signatures detected in source

○ Low Multiple Contributors 2.0

Single-author or unverifiable project

1 unique contributor(s) across 10 commits in dannybombastic/mcp-standard-ai
Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

x.AsyncClient: return httpx.AsyncClient( base_url=self.settings.base_url, he

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

' '.join(cmd)}") result = subprocess.run(cmd, capture_output=True, text=True, check=check) if res

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: example.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
Author name is missing or very short
Author "" 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-context-manager-mcp

Create a mini-application named 'AIAssetSync' that leverages the 'ai-context-manager-mcp' package to synchronize AI assets across multiple platforms. This application will serve as a bridge between different AI environments, ensuring that skills, prompts, specifications, and context data are consistently updated and accessible. Here’s a detailed breakdown of the steps and features:

1. **Setup Environment**: Begin by setting up your development environment. Ensure you have Python installed along with the 'ai-context-manager-mcp' package. If it's not available via pip, include instructions on how to install it from source.

2. **Define Asset Types**: Enumerate the types of AI assets that 'AIAssetSync' will manage. These include skills, prompts, specifications, and context data. Each asset type should have a corresponding class or structure defined within the application.

3. **Connection Management**: Use 'ai-context-manager-mcp' to establish connections with various AI environments. Implement a connection manager that can handle multiple simultaneous connections and ensure secure, efficient data transfer.

4. **Synchronization Logic**: Develop synchronization logic that ensures all AI assets are kept up-to-date across all connected environments. This includes handling updates, deletions, and additions of assets.

5. **User Interface**: Create a simple user interface for managing these assets. Users should be able to add new assets, view existing ones, and perform actions like updating or deleting assets.

6. **Error Handling & Logging**: Implement robust error handling and logging mechanisms to track any issues during synchronization processes. This will help in troubleshooting and maintaining the stability of the application.

7. **Testing & Validation**: Conduct thorough testing to validate the functionality of 'AIAssetSync'. This should include unit tests for individual components, integration tests for the synchronization process, and performance tests to ensure efficiency.

8. **Documentation**: Provide comprehensive documentation detailing how to use 'AIAssetSync', including setup instructions, usage examples, and API references if applicable.

The goal of this project is to demonstrate how 'ai-context-manager-mcp' can be effectively utilized to maintain consistency and accessibility of AI assets across diverse environments, making it easier for developers and users to manage and utilize these resources.