aiofmp

v1.3.0 safe
2.0
Low Risk

Asynchronous Financial Modeling Prep API Client with MCP Server

🤖 AI Analysis

Final verdict: SAFE

The package aiofmp v1.3.0 has been assessed and found to have minimal risks across all categories. It primarily interacts with external APIs via network requests, but there are no signs of malicious behavior or potential for supply-chain attacks.

  • Low network risk due to expected API interaction
  • No evidence of shell execution, obfuscation, or credential harvesting
Per-check LLM notes
  • Network: The use of aiohttp.ClientSession suggests the package performs network requests, which is common for packages interacting with external APIs.
  • Shell: No shell execution patterns detected, indicating no immediate risk from command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.

📦 Package Quality Overall: Medium (5.4/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/codemug/aiofmp#readme
  • Detailed PyPI description (24024 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 404 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 95 commits in codemug/aiofmp
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • ) self._session = aiohttp.ClientSession(timeout=timeout) self._session_owner = True
✓ 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: gmail.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 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 aiofmp 
Create a financial dashboard mini-app using Python's aiofmp package, which provides an asynchronous interface to the Financial Modeling Prep API. Your app should allow users to input ticker symbols of stocks they are interested in and display real-time stock prices, historical price data, and key financial metrics such as EPS, P/E ratio, and dividend yield. Additionally, implement a feature to visualize these metrics over time using matplotlib or a similar plotting library. Here’s how you can structure your project:

1. **Setup**: Begin by installing the aiofmp package via pip and setting up a virtual environment for your project.
2. **API Configuration**: Use aiofmp to fetch API keys from the Financial Modeling Prep documentation and configure them in your project.
3. **User Interface**: Develop a simple command-line interface where users can input ticker symbols and select which type of information they want to see (real-time price, historical data, financial metrics).
4. **Data Retrieval**: Implement functions that use aiofmp to asynchronously retrieve the selected data types based on user inputs.
5. **Data Visualization**: Create plots using matplotlib or another suitable library to visually represent the financial metrics over time.
6. **Error Handling**: Ensure robust error handling for cases like invalid ticker symbols or API rate limits.
7. **Testing**: Write tests to verify that your functions work correctly under various scenarios.
8. **Documentation**: Provide clear instructions on how to run your app and any necessary setup steps.

This project will showcase your ability to integrate third-party APIs, handle asynchronous requests efficiently, and present data in an intuitive manner.