analytics-agent-connector-bigquery

v0.3.0 safe
4.0
Medium Risk

BigQuery MCP connector for Analytics Agent

🤖 AI Analysis

Final verdict: SAFE

The package exhibits low risk in terms of network activity, shell execution, obfuscation, and credential handling. While there are concerns about metadata quality and maintenance, these do not strongly indicate malicious intent.

  • Low network and shell risk
  • No signs of obfuscation or credential harvesting
  • Metadata quality and maintenance are questionable
Per-check LLM notes
  • Network: No network calls suggest normal behavior for a package that does not require external services.
  • Shell: No shell execution detected, indicating the package operates within its defined scope without executing system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
  • Metadata: The package shows signs of low maintenance and poor metadata quality, but lacks clear indicators of malicious intent.

📦 Package Quality Overall: Low (1.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
○ 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
○ 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 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 analytics-agent-connector-bigquery 
Develop a data analysis tool called 'DataInsightPro' using the Python package 'analytics-agent-connector-bigquery'. This tool will allow users to connect to Google BigQuery datasets, perform complex queries, and visualize the results. Here are the key steps and features of the project:

1. **Setup Environment**: Install necessary Python packages including 'analytics-agent-connector-bigquery', pandas, matplotlib, and seaborn.
2. **Connection Setup**: Create a module to establish a secure connection to BigQuery using OAuth2 credentials provided by the user.
3. **Query Execution**: Implement a feature to allow users to input SQL-like queries directly into the application interface. Use 'analytics-agent-connector-bigquery' to execute these queries against BigQuery.
4. **Result Visualization**: Once the query is executed, display the results in various chart formats such as bar charts, line graphs, and pie charts using matplotlib and seaborn.
5. **Interactive Dashboard**: Develop an interactive dashboard where users can select different datasets, modify queries, and view real-time updates of their data visualizations.
6. **Export Functionality**: Add functionality to export query results and visualizations in formats like CSV, Excel, and PDF.
7. **Documentation & Testing**: Write comprehensive documentation for each module and test the application thoroughly to ensure reliability and accuracy.

This project leverages 'analytics-agent-connector-bigquery' to simplify the process of connecting to BigQuery and executing complex analytical queries, making it easier for users to gain insights from large datasets without needing extensive knowledge of BigQuery API or SQL.

💬 Discussion Feed

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