analytics-agent-connector-hive

v0.3.0 suspicious
4.0
Medium Risk

Hive / Kyuubi / Spark Thrift Server MCP connector for Analytics Agent

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to potential low maintainer effort and missing author information, which could indicate a less trustworthy or maintained project.

  • Metadata risk due to lack of author information and repository link.
  • Potential signs of low maintainer effort.
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of low maintainer effort and could potentially be suspicious due to lack of author information and repository link.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (1308 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ 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

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

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 8.0

4 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 analytics-agent-connector-hive 
Your task is to create a mini-application called 'HiveQueryRunner', which will allow users to interactively query a Hive database using the 'analytics-agent-connector-hive' package. This tool aims to simplify the process of querying data stored in Hive, making it accessible even to those who are not familiar with complex SQL commands or Hive configurations.

The application should have the following functionalities:
1. **Connection Management**: Allow users to connect to their Hive instance via the 'analytics-agent-connector-hive' package. Ensure that the connection parameters such as host, port, username, and password are securely handled.
2. **Query Execution**: Implement a feature where users can input their SQL queries directly into the application. The application should then execute these queries against the connected Hive database and display the results back to the user.
3. **Query History**: Maintain a history of executed queries along with their execution times and results. Users should be able to view past queries and rerun them if necessary.
4. **Result Visualization**: Provide basic visualization capabilities for the query results. For example, if the result is a dataset, allow users to visualize it as a chart or graph.
5. **Error Handling**: Robust error handling to ensure that any issues during the connection or query execution are clearly communicated to the user.
6. **User Interface**: Develop a simple and intuitive graphical user interface (GUI) using a Python library like Tkinter or PyQt. The GUI should be user-friendly and guide users through the process of connecting to Hive, executing queries, and viewing results.

In your implementation, focus on utilizing the 'analytics-agent-connector-hive' package effectively. Specifically, use its capabilities to manage connections to the Hive server and execute SQL queries. Additionally, explore how you can leverage the package's features to optimize performance and reliability of your application.

💬 Discussion Feed

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