A2A + MCP Dual Protocol Reference Agent: The Canonical Implementation

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Pipeline Architecture

As agent systems mature, two protocol layers matter most: A2A for inter-agent coordination and MCP for tool interaction. This reference project demonstrates both in one implementation you can actually run and inspect.

It is useful as a learning baseline and as a template for production agents that need explicit protocol boundaries.

Architecture in Practice

A2A handles discovery and task routing. MCP handles typed tool invocation and result handoff into the reasoning loop. Keeping those concerns separate reduces integration ambiguity as systems scale.

The included dashboard and mock mode also make protocol behavior observable during local development.

Key Endpoints

GET  /.well-known/agent-card.json
POST /tasks/send
GET  /tasks/{task_id}
POST /tasks/{task_id}/cancel
GET  /health

Run the Project

git clone https://github.com/dakshjain-1616/a2a-mcp-reference-agent.git
cd a2a-mcp-reference-agent
make install
cp .env.example .env

make serve-mock
# or separately
make serve-a2a
make serve-mcp
make serve-ui

The current README uses Makefile-driven workflows and agent-card.json discovery for A2A, and the run instructions above follow that default setup.

Architecture Walkthrough

The a2a mcp dual protocol reference agent repository is organized around a clear pipeline, so you can trace the full flow from input handling to final output without guesswork. This makes onboarding easier for new contributors and helps teams debug faster when behavior changes after updates.

Practical Use Cases

If you are evaluating a2a mcp dual protocol reference agent for production, start with a small real-world dataset, run the included commands end to end, and compare output quality, latency, and operational complexity. This gives a practical signal that is stronger than a toy demo.

Implementation Notes

The project is useful as both a standalone tool and a reference implementation. You can copy patterns from this codebase into your own stack, especially around evaluation discipline, reproducibility, and operator visibility.

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