Google A2A Agent Example MCP Server

@system32miro

Basic information

About Google A2A Agent Example MCP Server

Example implementation of the Google A2A protocol with a Flask server and Python client. Includes web search integration via MCP

#google-a2a#a2a-communication#flask-server

Google A2A Agent Example MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "google-a2a-agent": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

Google A2A Agent Example MCP Server tools

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Overview

What is Google A2A Agent Example?

Google A2A Agent Example implements a simple Agent-to-Agent (A2A) communication system based on Google's proposed A2A protocol. It consists of a Flask-based server that acts as an AI agent and a Python client that demonstrates agent discovery and task exchange.

How to use Google A2A Agent Example?

Clone the repository, create a virtual environment, add your OpenAI and Brave API keys to a .env file, and install dependencies. Run python server.py in one terminal and python client.py in a separate terminal to send a predefined question and receive the agent's response.

Key features of Google A2A Agent Example

Implements core Google A2A protocol endpoints (/.well-known/agent.json, /tasks/send)
Uses OpenAI's GPT-4o-mini for AI query processing
Integrates Brave Search via MCP (Model Context Protocol) for web searches
Provides a client script demonstrating agent discovery and task submission
Manages external tool interactions through the pydantic-ai library

Use cases of Google A2A Agent Example

Prototyping agent-to-agent communication with Google's A2A standard
Building an AI agent that can perform web searches via Brave Search
Demonstrating agent discovery and task exchange for developer education

Frequently asked questions

What is the Google A2A protocol?

A2A is Google's proposed standard for enabling AI agents to communicate with each other through a standardized HTTP API, using an Agent Card for discovery and a Task API for exchanging messages.

What API keys are required?

You need an OpenAI API key (for GPT-4o-mini) and a Brave API key (for Brave Search), stored in a `.env` file as `OPENAI_API_KEY` and `BRAVE_API_KEY`.

How does the architecture work?

The client discovers the agent by fetching its Agent Card, sends a task to `/tasks/send`, the server processes the query using OpenAI and optionally calls Brave Search via MCP, and returns the final response to the client.

Does this implementation cover the full A2A protocol?

No, this example only implements the essential `/tasks/send` endpoint and Agent Card discovery, not the full A2A protocol specification.

Where does data processing occur?

All AI processing and tool integration (OpenAI API calls and Brave Search requests) happen server-side. The client only sends the initial task and receives the final response.

Comments

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