aind-mgi-service-async-client

v0.2.5 safe
2.0
Low Risk

aind-mgi-service

🤖 AI Analysis

Final verdict: SAFE

The package appears legitimate and poses minimal risk based on the analysis of its network, shell, obfuscation, and credential usage patterns. There are no clear signs of malicious intent.

  • Low network risk
  • No shell execution patterns
  • No obfuscation
  • No credential harvesting patterns
Per-check LLM notes
  • Network: The use of aiohttp.ClientSession with TCPConnector is typical for making HTTP requests asynchronously and does not inherently indicate malicious activity.
  • Shell: No shell execution patterns were detected, which is normal and expected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: Low activity and lack of detailed metadata suggest potential low effort or new project, but no clear indicators of malicious intent.

📦 Package Quality Overall: Low (3.6/10)

✦ High Test Suite 9.0

Test suite present — 7 test file(s) found

  • 7 test file(s) detected (e.g. test_default_api.py)
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ 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

  • 30 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 1.5

Found 1 network call pattern(s)

  • self.pool_manager = aiohttp.ClientSession( connector=aiohttp.TCPConnector(limit=self.m
✓ 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: openapitools.org

✓ 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 "OpenAPI Generator community" 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 aind-mgi-service-async-client 
Create a fully-functional mini-application named 'MGI Explorer' using Python's 'aind-mgi-service-async-client' package. This application will serve as a tool for researchers and developers to explore and interact with data from the Mouse Genome Informatics (MGI) database in an asynchronous manner, enhancing performance and user experience. Your task is to design and implement this application following these steps and suggestions:

1. **Setup Environment**: Ensure you have Python installed and create a virtual environment. Install the necessary packages including 'aind-mgi-service-async-client'.

2. **Application Structure**: Design your application to include modules for handling asynchronous requests, parsing responses, and displaying results. Consider implementing a command-line interface (CLI) for simplicity.

3. **Core Functionality**: Implement functions to query MGI for specific gene information, phenotypes, and annotations. Use the 'aind-mgi-service-async-client' package to handle these queries asynchronously.

4. **Feature Suggestions**:
   - **Query Interface**: Allow users to input gene names or IDs and retrieve detailed information about genes, including their associated phenotypes and annotations.
   - **Search Filters**: Provide options to filter search results based on criteria such as phenotype types, gene function categories, or organism.
   - **Output Format**: Offer options for output formats like JSON, CSV, or plain text.
   - **Error Handling**: Implement robust error handling to manage issues like invalid inputs, network errors, or server-side errors gracefully.

5. **User Interaction**: Enhance the user interaction by providing clear instructions and feedback messages. For example, display a message when a query is being processed and another upon completion or failure.

6. **Testing and Validation**: Test your application thoroughly with various inputs to ensure reliability and accuracy of the outputs. Validate the data returned from MGI against known datasets or manually curated information.

7. **Documentation**: Write comprehensive documentation detailing how to install, configure, and use the 'MGI Explorer' application. Include examples and best practices for interacting with the MGI database through the CLI.

8. **Deployment**: Prepare your application for deployment by packaging it into a distributable format such as a Python package or a Docker container. Ensure it is easily accessible to other researchers and developers.

By completing these steps, you will have developed a powerful tool that leverages the capabilities of 'aind-mgi-service-async-client' to provide efficient access to MGI data, benefiting the scientific community.

💬 Discussion Feed

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