analytics-mcp

v0.6.0 suspicious
4.0
Medium Risk

MCP server for Google Analytics

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks for obfuscation and credential harvesting, but the incomplete metadata and potential inactivity of the author raise concerns about its origin and maintenance.

  • Incomplete author information
  • Potential inactivity of the author
Per-check LLM notes
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author's information is incomplete and they appear to be new or inactive, which raises some suspicion but not enough to conclusively indicate malicious intent.

📦 Package Quality Overall: Medium (5.0/10)

○ 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 (6646 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 13 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 9 unique contributor(s) across 59 commits in googleanalytics/google-analytics-mcp
  • Active community — 5 or more distinct 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

Email domain looks legitimate: users.noreply.github.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository googleanalytics/google-analytics-mcp appears legitimate

⚠ 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 analytics-mcp 
Develop a real-time data analysis dashboard using the 'analytics-mcp' package in Python. This application will serve as a bridge between your local environment and Google Analytics, allowing you to fetch, process, and visualize data from Google Analytics in real-time. Here’s a step-by-step guide on how to create this application:

1. **Setup**: Install the necessary packages including 'analytics-mcp', 'Flask' for web framework, and 'matplotlib' for visualization. Ensure you have access to a Google Analytics account.
2. **Authentication**: Implement OAuth 2.0 authentication flow to authorize the application to access Google Analytics data. Use 'analytics-mcp' to handle the connection and data fetching efficiently.
3. **Data Fetching**: Utilize 'analytics-mcp' to fetch real-time data from Google Analytics. Focus on metrics such as page views, unique visitors, and bounce rate.
4. **Data Processing**: Process the fetched data to calculate additional metrics like session duration and engagement rates. Store these processed results in a simple database or cache system for quick retrieval.
5. **Web Interface**: Build a web interface using Flask to display the real-time data. Use HTML, CSS, and JavaScript to create interactive charts and graphs using Matplotlib or other visualization libraries.
6. **Real-Time Updates**: Implement real-time updates on the dashboard by periodically refreshing the data from Google Analytics using 'analytics-mcp'. Consider using WebSockets for more dynamic updates.
7. **Customization**: Allow users to customize the dashboard by selecting which metrics they want to track and how they want to visualize them. Provide options to save these preferences.
8. **Testing & Deployment**: Test the application thoroughly to ensure it works correctly and efficiently. Deploy the application on a cloud platform like AWS or Heroku.

This project aims to showcase the capabilities of 'analytics-mcp' in handling real-time data from Google Analytics and integrating it into a useful, real-world application.

💬 Discussion Feed

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