A2A + Klavis AI: Complete MCP Integration
A2A is a MCP server that An MCP server that bridges the Model Context Protocol (MCP) with the Agent-to-Agent (A2A) protocol, enabling MCP-compatible AI assistants (like Claude) to seamlessly interact with A2A agents..
When integrated with Klavis AI, you can:
- Register and manage A2A agents for communication
- Send messages to A2A agents and handle responses
- Manage asynchronous tasks and retrieve results
This guide provides step-by-step instructions to set up A2A in Klavis AI, including configuration, examples, and troubleshooting.
What You'll Achieve
After completing this setup:
- A2A will be fully integrated and operational
- You can use A2A tools directly in Klavis AI
- All A2A capabilities will be available for your workflows
- Access to 4 different tools
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 A2A
Configuration
Step 2: Configure Klavis AI
- Open Klavis AI settings
- Navigate to MCP server configuration
- Add A2A server with appropriate settings
- Save and restart if needed
Examples
Once configured, you can use A2A in Klavis AI:
Register and Communicate with Agent
Set up communication with an A2A agent
Ask Klavis AI: "Register an agent for data analysis tasks and send it a dataset processing request"
Expected Result: Agent registered, task submitted, and task_id returned for tracking
Stream Real-Time Data
Use streaming to get real-time updates from agent
Ask Klavis AI: "Stream live updates from the monitoring agent about system metrics"
Expected Result: Continuous stream of system metrics and status updates
Manage Multiple Agents
Coordinate tasks across multiple A2A agents
Ask Klavis AI: "List all available agents and distribute parallel processing tasks"
Expected Result: Agent list displayed, tasks distributed, and progress tracked
Handle Task Results
Retrieve and process completed task results
Ask Klavis AI: "Get the results from the data analysis task I submitted earlier"
Expected Result: Task results retrieved with processed data and analysis findings
Testing Your Setup
- Launch Klavis AI
- Verify A2A is available in the tools list
- Test basic A2A functionality
Troubleshooting
Common Issues
Agent Registration Failed
Symptoms: Registration errors, Agent not found, Connection timeouts
Cause: Network connectivity issues or invalid agent configuration
Solution:
- Check internet connectivity to A2A network
- Verify agent endpoint URLs are correct
- Ensure agent is online and accepting connections
- Check firewall settings and port accessibility
Task Never Completes
Symptoms: Task stuck in pending state, No response from agent
Cause: Agent overload, network issues, or task complexity
Solution:
- Check agent status and availability
- Cancel and resubmit the task
- Break complex tasks into smaller parts
- Try different agent if available
Transport Protocol Errors
Symptoms: Connection refused, Protocol mismatch errors
Cause: Incorrect transport configuration or port conflicts
Solution:
- Verify MCP_TRANSPORT setting matches client expectations
- Check MCP_HOST and MCP_PORT are accessible
- Ensure no port conflicts with other services
- Try different transport mode (stdio, http, sse)
Message Streaming Issues
Symptoms: Broken streams, Incomplete messages, Timeout errors
Cause: Network instability or buffer overflow
Solution:
- Check network stability and bandwidth
- Reduce message frequency or size
- Enable debug logging to trace issues
- Use appropriate buffer sizes for streaming
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>
A2A not appearing in Klavis AI
Symptoms: Server not listed, Tools not available
Cause: Configuration or installation issue
Solution:
- Verify configuration syntax
- Check A2A installation
- Restart Klavis AI
- Check logs for error messages
Next Steps
Now that A2A is integrated with Klavis AI:
- Explore all A2A 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 A2A development
Need Help?
- Search for A2A documentation
- Check the Klavis AI MCP guide
- Join the MCP community discussions