🤖 AI Analysis

Final verdict: SAFE

The package appears to be legitimate with minimal risks identified. The primary concern lies in the metadata which suggests it might be new or less active, but there are no indications of malicious activities.

Low network, shell, obfuscation, and credential risks.
Metadata shows some red flags, possibly due to the package being new or less active.

Per-check LLM notes

Network: The presence of network calls is not inherently suspicious but should be reviewed to ensure they align with the package's intended functionality.
Shell: No shell execution patterns detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows some red flags due to the lack of author information and a single associated package, indicating potential low activity or newness.

📦 Package Quality Overall: Medium (5.2/10)

✦ High Test Suite 9.0

Test suite present — 4 test file(s) found

Test runner config found: pyproject.toml
4 test file(s) detected (e.g. test_local_auth.py)

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Docs" -> https://docs.intellegens.com
Brief PyPI description (268 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Development Status classifier >= Beta

◈ Medium Type Annotations 5.0

Partial type annotation coverage

83 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 9.0

Found 6 network call pattern(s)

suer realm.""" async with httpx.AsyncClient(verify=verify_tls, timeout=10.0) as client: response
cepted """ async with httpx.AsyncClient() as client: response = await client.post(
ver_url: str): async with httpx.AsyncClient() as client: response = await client.post(
st user", []) async with httpx.AsyncClient() as client: response = await client.post(
it(".")[:-1]) async with httpx.AsyncClient() as client: response = await client.post(
e", key=None) async with httpx.AsyncClient() as client: response = await client.post(

✓ 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

Email domain looks legitimate: intellegens.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

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 alchemite-mcp

Create a fully-functional mini-application named 'Alchemite Workbench' using the Python package 'alchemite-mcp'. This application will serve as a user-friendly interface for local deployment and management of Alchemite MCP services. The application should allow users to easily start, stop, and manage their Alchemite MCP instances directly from a graphical user interface or command line interface.

Key Features:
1. User Authentication: Implement basic user authentication to ensure only authorized users can manage the Alchemite MCP instances.
2. Instance Management: Allow users to create, start, stop, and delete Alchemite MCP instances with ease.
3. Configuration Editor: Provide a simple editor where users can modify the configuration settings of their Alchemite MCP instances before starting them.
4. Monitoring Dashboard: Develop a monitoring dashboard that displays real-time status, logs, and performance metrics of running Alchemite MCP instances.
5. Backup and Restore: Enable users to backup and restore their Alchemite MCP instance configurations and data.
6. Documentation: Include comprehensive documentation explaining how to install, configure, and use the 'Alchemite Workbench' application.

Utilization of 'alchemite-mcp':
- Use the 'alchemite-mcp' package to handle the core functionalities of managing Alchemite MCP instances. This includes initializing new instances, applying configurations, and controlling their lifecycle operations such as starting and stopping.
- Integrate the package's APIs or command-line tools into your application's backend logic to automate these processes and provide seamless integration with the user interface elements mentioned above.

The goal is to develop a versatile and user-friendly tool that simplifies the process of working with Alchemite MCP for both novice and experienced users.