Apache Gravitino + Klavis AI: Complete MCP Integration
Apache Gravitino is a MCP server that Allow LLMs to explore metadata of structured data and unstructured data with Gravitino, and perform data governance tasks including tagging/classification..
When integrated with Klavis AI, you can:
- Access Apache Gravitino functionality through Klavis AI
This guide provides step-by-step instructions to set up Apache Gravitino in Klavis AI, including configuration, examples, and troubleshooting.
What You'll Achieve
After completing this setup:
- Apache Gravitino will be fully integrated and operational
- You can use Apache Gravitino tools directly in Klavis AI
- All Apache Gravitino capabilities will be available for your workflows
Prerequisites
Before starting, ensure you have:
- Klavis AI installed and configured
- Compatible operating system (Cloud (klavis.ai), Self-Hosted (Docker), Python SDK, TypeScript SDK)
Installation
Step 1: Install Apache Gravitino
Configuration
Step 2: Configure Klavis AI
- Open Klavis AI settings
- Navigate to MCP server configuration
- Add Apache Gravitino server with appropriate settings
- Save and restart if needed
Examples
Once configured, you can use Apache Gravitino in Klavis AI:
Testing Your Setup
- Launch Klavis AI
- Verify Apache Gravitino is available in the tools list
- Test basic Apache Gravitino functionality
Troubleshooting
Common Issues
API Authentication Failed
Symptoms: 401 Unauthorized errors, Invalid API key message
Cause: Missing or incorrect API key
Solution:
- Verify API key is correctly copied from klavis.ai dashboard
- Check API key is set in environment variable or code
- Ensure API key has not expired
- Regenerate API key if necessary
OAuth Authorization Errors
Symptoms: OAuth redirect failures, Authentication loop, Token refresh errors
Cause: OAuth credentials not configured or expired
Solution:
- Complete OAuth authorization flow for required services
- Check redirect URLs are correctly configured
- Verify OAuth app credentials in service provider settings
- Contact Klavis support for OAuth troubleshooting
Tool Call Failures
Symptoms: Tool not found errors, Execution timeouts, Rate limit errors
Cause: Tool not properly configured or service rate limits exceeded
Solution:
- Verify tool name matches available tools list
- Check service-specific rate limits and quotas
- Ensure required parameters are provided correctly
- Review Klavis API logs for detailed error messages
Context Window Overload
Symptoms: Too many tools error, Context length exceeded, Slow responses
Cause: Too many tools exposed to AI agent at once
Solution:
- Use Strata Progressive Discovery to expose tools hierarchically
- Reduce number of simultaneous MCP servers in Strata configuration
- Filter tools based on current task context
- Enable intelligent tool filtering in Strata settings
Docker Deployment Issues
Symptoms: Container fails to start, Port conflicts, Connection refused
Cause: Docker configuration or port binding errors
Solution:
- Ensure Docker is installed and running
- Check port 5000 is not already in use (or change port mapping)
- Verify Docker image pulled successfully
- Check container logs: docker logs <container-id>
Apache Gravitino not appearing in Klavis AI
Symptoms: Server not listed, Tools not available
Cause: Configuration or installation issue
Solution:
- Verify configuration syntax
- Check Apache Gravitino installation
- Restart Klavis AI
- Check logs for error messages
Next Steps
Now that Apache Gravitino is integrated with Klavis AI:
- Explore all Apache Gravitino capabilities through Klavis AI
- Check out other MCP servers that work with Klavis AI
- Join the MCP community for tips and support
- Consider contributing to Apache Gravitino development
Need Help?
- Search for Apache Gravitino documentation
- Check the Klavis AI MCP guide
- Join the MCP community discussions