🤖 AI Analysis

Final verdict: SAFE

The package has minimal risks associated with it, primarily concerning credential handling and metadata quality. There is no evidence of malicious activity or supply-chain attack.

Low risk scores across all categories except metadata.
No shell execution or obfuscation patterns detected.

Per-check LLM notes

Network: The network call pattern indicates the package is likely making HTTP requests with retry logic, which is common for legitimate functionality like API calls.
Shell: No shell execution patterns detected, suggesting no immediate risk from command execution.
Obfuscation: No obfuscation patterns detected in the provided code snippet.
Credentials: The code appears to be prompting for a password input which is a common practice for authentication but should ensure proper handling and storage of credentials.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, but there are no clear indicators of malicious intent.

📦 Package Quality Overall: Low (4.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

2 test file(s) detected (e.g. test_export.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (12827 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

126 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 6.0

Limited contributor diversity

2 unique contributor(s) across 34 commits in weironz/appflowy_mcp
Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

) self._http_client = httpx.Client(timeout=60.0) self._max_retries = 3 self._ba

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

password = args.password or getpass.getpass("Password: ") if args.base_url: client.base_url

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ 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

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with appflowy-mcp

Create a fully-functional mini-application using the 'appflowy-mcp' Python package. This application will serve as a basic content management system (CMS) for managing workspaces, spaces, pages, databases, and rows within those databases. Here's a detailed breakdown of the requirements and steps to build this application:

1. **Setup Environment**: Ensure you have Python installed on your machine. Install the necessary dependencies including 'appflowy-mcp'.
2. **Define Core Features**:
- Users should be able to create new workspaces.
- Within each workspace, users can create multiple spaces.
- Each space can contain one or more pages.
- Databases can be added to any page, allowing for structured data storage.
- Rows within databases can be added, edited, deleted, and queried.
3. **User Interface**:
- Develop a simple command-line interface (CLI) or a basic web interface using Flask/Django for user interaction.
4. **Database Operations**:
- Implement functionality to add, edit, delete, and query rows in the database.
5. **Security Considerations**:
- Ensure that only authorized users can perform certain actions like deleting a workspace or modifying a database.
6. **Testing**:
- Write unit tests to ensure all functions work as expected.
7. **Documentation**:
- Provide clear documentation on how to install and use the application.

How 'appflowy-mcp' is Utilized:
- Use 'appflowy-mcp' to handle the backend logic for creating, updating, and managing workspaces, spaces, pages, databases, and rows. This includes leveraging its API to perform CRUD (Create, Read, Update, Delete) operations efficiently.
- For the CLI version, commands such as 'create_workspace', 'add_space', 'edit_page', etc., should be implemented to interact with 'appflowy-mcp'.
- In the case of a web interface, APIs exposed by 'appflowy-mcp' should be consumed via HTTP requests to manage the content.

This project aims to showcase the capabilities of 'appflowy-mcp' in managing complex hierarchical data structures and to provide a practical example of integrating it into a real-world application.