AgentQL + gptme: Complete MCP Integration
AgentQL is a MCP server that Enable AI agents to get structured data from unstructured web with AgentQL..
When integrated with gptme, you can:
- Extract structured data from web pages using natural language
- Scrape web data with structured field definitions
- Monitor web pages for content changes
This guide provides step-by-step instructions to set up AgentQL in gptme, including configuration, examples, and troubleshooting.
What You'll Achieve
After completing this setup:
- AgentQL will be fully integrated and operational
- You can use AgentQL tools directly in gptme
- All AgentQL capabilities will be available for your workflows
- Access to 3 different tools
Prerequisites
Before starting, ensure you have:
- API key from AgentQL Dev Portal
- gptme installed and configured
- Compatible operating system (Terminal, Python, Cross-platform)
Installation
Step 1: Install AgentQL
Configuration
Step 2: Configure gptme
- Open gptme settings
- Navigate to MCP server configuration
- Add AgentQL server with appropriate settings
- Save and restart if needed
Examples
Once configured, you can use AgentQL in gptme:
E-commerce Product Monitoring
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Ask gptme: "Extract product details from Amazon search results"
Expected Result: undefined
News Article Analysis
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Ask gptme: "Get headlines, summaries, and publish dates from news site"
Expected Result: undefined
Social Media Insights
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Ask gptme: "Extract post engagement from social media feeds"
Expected Result: undefined
Testing Your Setup
- Launch gptme
- Verify AgentQL is available in the tools list
- Test basic AgentQL functionality
Troubleshooting
Common Issues
Installation Failed
Symptoms: pip install errors, Missing dependencies
Cause: Python environment or package conflicts
Solution:
- Use virtual environment for clean installation
- Update pip to latest version
- Install with pipx for isolation
- Check Python version compatibility
API Key Issues
Symptoms: Authentication errors, Model access denied
Cause: Missing or invalid API keys
Solution:
- Verify API keys are set correctly
- Check API key permissions and quotas
- Test API keys with curl or other tools
- Review model provider documentation
MCP Server Not Loading
Symptoms: Server load errors, Tools not available
Cause: Server configuration or installation issues
Solution:
- Verify server installation and PATH
- Check MCP server configuration syntax
- Test server independently before gptme integration
- Review gptme logs for connection errors
Terminal Display Issues
Symptoms: Formatting problems, Character encoding errors
Cause: Terminal compatibility or encoding issues
Solution:
- Ensure terminal supports UTF-8 encoding
- Try different terminal applications
- Check terminal color and formatting settings
- Update terminal application to latest version
AgentQL not appearing in gptme
Symptoms: Server not listed, Tools not available
Cause: Configuration or installation issue
Solution:
- Verify configuration syntax
- Check AgentQL installation
- Restart gptme
- Check logs for error messages
Next Steps
Now that AgentQL is integrated with gptme:
- Explore all AgentQL capabilities through gptme
- Check out other MCP servers that work with gptme
- Join the MCP community for tips and support
- Consider contributing to AgentQL development
Need Help?
- Search for AgentQL documentation
- Check the gptme MCP guide
- Join the MCP community discussions