appkit-mcp-charts

v1.11.3 safe
3.0
Low Risk

MCP Charts & Visualization Server

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity such as network calls, shell execution, or credential harvesting. The metadata risk is slightly elevated due to the maintainer having only one package, but this alone does not indicate a supply-chain attack.

  • No network calls detected.
  • No shell executions detected.
  • Low obfuscation risk.
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell executions detected, indicating the package does not execute external commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has only one package, which may indicate a new or less active account.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 3 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/jenreh/appkit/tree/main/docs
  • Detailed PyPI description (1395 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

  • 28 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in jenreh/appkit
  • Small but multi-author team (3–4 contributors)

🔬 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

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository jenreh/appkit appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Jens Rehpöhler" 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 appkit-mcp-charts 
Your task is to create a versatile dashboard application using Python, which leverages the 'appkit-mcp-charts' package for data visualization. This application will serve as a monitoring tool for various datasets, allowing users to visualize and analyze data trends in real-time. The dashboard will be designed to be user-friendly and interactive, enabling users to select different datasets and apply various visualizations on the fly.

### Core Features:
1. **Data Importation**: Allow users to upload CSV or Excel files containing dataset information.
2. **Visualization Options**: Implement a variety of chart types such as line charts, bar charts, pie charts, and scatter plots using 'appkit-mcp-charts'. Each type should have customizable options like color schemes, axis labels, and title.
3. **Real-Time Updates**: If possible, implement functionality to fetch live data from a predefined source (e.g., a REST API) and update the dashboard in real-time.
4. **User Interface**: Design a clean and intuitive UI where users can navigate through different visualizations easily.
5. **Export Functionality**: Provide an option for users to export their visualizations as images or PDFs.

### Steps to Build the Application:
1. **Setup Environment**: Ensure Python and 'appkit-mcp-charts' are installed. You might also need other libraries like pandas for data manipulation and Flask for web development.
2. **Data Handling**: Create functions to handle file uploads and process the data into a format suitable for visualization.
3. **Chart Generation**: Utilize 'appkit-mcp-charts' to generate charts based on user selections. Customize each chart according to user preferences.
4. **UI Development**: Use HTML/CSS/JavaScript along with Flask to create the front-end interface. Ensure the interface is responsive and easy to use.
5. **Integration**: Integrate all components together so that the user can upload data, choose visualization types, customize settings, and view/export results seamlessly.
6. **Testing**: Thoroughly test the application to ensure all functionalities work correctly and efficiently.
7. **Documentation**: Provide clear documentation on how to install and use the application, including any prerequisites and setup instructions.

By following these steps and utilizing the 'appkit-mcp-charts' package effectively, you'll develop a powerful and flexible dashboard application that can be used in various industries for data analysis and presentation.

💬 Discussion Feed

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