analog-mcp

v0.1.0 safe
3.0
Low Risk

Model Context Protocol server for Analog — expose webpage extraction to MCP-compatible AI agents.

🤖 AI Analysis

Final verdict: SAFE

The package exhibits minimal risks across all categories with no network calls, shell executions, or obfuscations detected. The metadata suggests some lack of effort in documentation but does not indicate malicious intent.

  • Low network and shell execution risk
  • No signs of obfuscation or credential harvesting
  • Minimal metadata effort
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external communication.
  • Shell: No shell execution patterns detected, indicating the package does not execute commands on the host system.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows low effort and potentially unverified author information, but lacks clear red flags like typosquatting or suspicious links.

📦 Package Quality Overall: Low (3.6/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

  • Test runner config found: pyproject.toml
  • 1 test file(s) detected (e.g. test_smoke.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (2459 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

Email domain looks legitimate: getanalog.io>

✓ 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 analog-mcp 
Create a web-based application that allows users to extract content from web pages and feed this data into AI agents compatible with the Model Context Protocol (MCP). This application will leverage the 'analog-mcp' Python package to handle the communication between the web interface and the AI agents. Here’s a step-by-step guide on how to build this mini-app:

1. **Setup Project Environment**: Begin by setting up a virtual environment for your project and installing necessary packages including 'analog-mcp'. Ensure you also have Flask or Django installed for creating the web application.
2. **Design Web Interface**: Develop a user-friendly web interface where users can input URLs of web pages they wish to extract content from. Include fields for specifying which parts of the page (e.g., text, images, links) to extract.
3. **Integrate 'analog-mcp'**: Use the 'analog-mcp' package to set up a server that listens for requests from the web interface. When a request is made, the server should use 'analog-mcp' to extract the specified content from the URL provided.
4. **Data Processing**: Implement functionality within your app to process the extracted data. This could include cleaning the text, resizing images, or extracting metadata from links.
5. **AI Agent Integration**: Once processed, the data should be formatted according to MCP standards and sent to an AI agent for further analysis or action. Ensure your application supports at least one type of AI agent, such as a language model or image classifier.
6. **User Feedback**: Provide users with feedback on the status of their requests (e.g., processing, complete, error). Allow them to view the results of the AI agent’s actions based on the extracted data.
7. **Security Measures**: Implement basic security measures such as input validation and sanitization to protect against common web vulnerabilities.
8. **Testing and Deployment**: Thoroughly test your application to ensure it works as expected. Deploy the application using a service like Heroku or AWS, making sure to configure any necessary environment variables.

Suggested Features:
- Support for multiple AI agents through configuration files.
- User authentication and session management.
- Logging and monitoring of API calls and data processing.
- Option to schedule periodic extractions for regular updates.

By following these steps and utilizing the 'analog-mcp' package effectively, you will create a powerful tool for integrating web scraping capabilities with AI-driven workflows.

💬 Discussion Feed

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