A2A + mcp-agent: 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 mcp-agent, 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 mcp-agent, 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 mcp-agent
• All A2A capabilities will be available for your workflows
• Access to 4 different tools

Prerequisites

Before starting, ensure you have:

• mcp-agent installed and configured
• Compatible operating system (Python 3.9+, pip Installation, uv Installation, Temporal (Optional))

Installation

Step 1: Install A2A

Configuration

Step 2: Configure mcp-agent

1. Open mcp-agent settings
2. Navigate to MCP server configuration
3. Add A2A server with appropriate settings
4. Save and restart if needed

Examples

Once configured, you can use A2A in mcp-agent:

Register and Communicate with Agent

Set up communication with an A2A agent

Ask mcp-agent: "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 mcp-agent: "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 mcp-agent: "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 mcp-agent: "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

1. Launch mcp-agent
2. Verify A2A is available in the tools list
3. 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

Import Error - Module Not Found

Symptoms: ModuleNotFoundError: No module named mcp_agent, Import fails

Cause: mcp-agent not installed or wrong Python environment

Solution:

• Verify installation: pip show mcp-agent
• Ensure using correct Python environment/virtualenv
• Reinstall: pip install --upgrade mcp-agent
• Check Python version is 3.9 or higher

MCP Server Connection Failures

Symptoms: Server not starting, Connection timeout, Tool execution errors

Cause: Invalid MCP server configuration or missing dependencies

Solution:

• Verify npx is installed and accessible (required for stdio servers)
• Check MCP server package name and arguments are correct
• Test server independently: npx -y @modelcontextprotocol/server-filesystem /path
• Review agent logs for detailed error messages
• Ensure required environment variables are set

LLM API Authentication Errors

Symptoms: 401 Unauthorized, API key invalid, Provider authentication failed

Cause: Missing or incorrect API keys for LLM provider

Solution:

• Set environment variables: ANTHROPIC_API_KEY, OPENAI_API_KEY, etc.
• Verify API key is valid and has correct permissions
• Check API key format matches provider requirements
• Pass api_key parameter explicitly in Agent initialization

Workflow Execution Failures

Symptoms: Workflow hangs, Timeout errors, Incomplete results

Cause: Workflow pattern misconfiguration or task complexity

Solution:

• Increase timeout values for long-running tasks
• Break complex workflows into smaller steps
• Verify all workflow dependencies are available
• Check Temporal backend is running (for production deployments)
• Review workflow logs for specific error messages

Temporal Backend Connection Issues

Symptoms: Cannot connect to Temporal, Workflow registration fails, Worker errors

Cause: Temporal server not running or misconfigured

Solution:

• Verify Temporal server is running: temporal server health
• Check Temporal connection settings (host, port, namespace)
• Ensure Temporal workers are registered correctly
• Review Temporal server logs for connection errors
• Start local Temporal dev server: temporal server start-dev

Performance Issues with Large Workflows

Symptoms: Slow execution, High memory usage, Timeout errors

Cause: Inefficient workflow design or resource constraints

Solution:

• Use map-reduce pattern for parallelizable tasks
• Implement batching for large data processing
• Enable Temporal backend for better resource management
• Monitor agent memory usage and optimize task size
• Consider streaming responses for large outputs

A2A not appearing in mcp-agent

Symptoms: Server not listed, Tools not available

Cause: Configuration or installation issue

Solution:

• Verify configuration syntax
• Check A2A installation
• Restart mcp-agent
• Check logs for error messages

Next Steps

Now that A2A is integrated with mcp-agent:

• Explore all A2A capabilities through mcp-agent
• Check out other MCP servers that work with mcp-agent
• Join the MCP community for tips and support
• Consider contributing to A2A development

Need Help?

• Search for A2A documentation
• Check the mcp-agent MCP guide
• Join the MCP community discussions