https://github.com/lazauk/hackster-aiagenticprotocols--mcp-a2a-acp
Hackster Learning Series about AI Agentic protocols (MCP, A2A, ACP) with practical code examples.
https://github.com/lazauk/hackster-aiagenticprotocols--mcp-a2a-acp
a2a acp agents ai azure hackster mcp openai protocols
Last synced: 3 months ago
JSON representation
Hackster Learning Series about AI Agentic protocols (MCP, A2A, ACP) with practical code examples.
- Host: GitHub
- URL: https://github.com/lazauk/hackster-aiagenticprotocols--mcp-a2a-acp
- Owner: LazaUK
- License: mit
- Created: 2025-06-29T20:31:12.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-07-07T14:50:12.000Z (3 months ago)
- Last Synced: 2025-07-07T15:50:00.477Z (3 months ago)
- Topics: a2a, acp, agents, ai, azure, hackster, mcp, openai, protocols
- Language: Python
- Homepage:
- Size: 44.9 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Hackster Learning Series: AI Agentic Protocols
This GitHub repo complements The Learning Series articles on [Hackster.io](https://www.hackster.io/) about **AI Agentic Protocols**. It covers:
- the **Model Context Protocol (MCP)** for dynamic tool and data access,
- the **Agent2Agent Protocol (A2A)** for direct agent collaboration,
- and the **Agent Communication Protocol (ACP)** for robust, interoperable communication.
The repo includes Python code to jump-start practical implementation of these protocols, offering immediate application opportunities.
## 📑 Table of contents:
- [Environment Setup](#environment-setup)
- [Part 1: Model Context Protocol (MCP)](#part-1-model-context-protocol-mcp)
- [Part 2: Agent2Agent Protocol (A2A)](#part-2-agent2agent-protocol-a2a)
- [Part 3: Agent Communication Protocol (ACP)](#part-3-agent-communication-protocol-acp)
- [Demo videos on YouTube](#demo-videos-on-youtube)
## Environment Setup
1. Install the required Python packages, listed in the provided *requirements.txt*:
``` PowerShell
pip install -r requirements.txt
```
2. If using Azure OpenAI as your AI backend, set the following environment variables:
| Variable | Description |
| ----------------------- | ------------------------------------------------ |
| `AOAI_API_BASE` | Base URL of the Azure OpenAI endpoint |
| `AOAI_API_VERSION` | API version of the Azure OpenAI endpoint |
| `AOAI_DEPLOYMENT` | Deployment name of the Azure OpenAI model |
## Part 1: Model Context Protocol (MCP)
This section demonstrates how an AI agent can dynamically discover and use external tools. The implementation uses an **MCP Server** (`MCPServer_HomeAutomation.py`) to expose home automation functionalities (tools) and an **MCP Client** (`MCPClient_GradioUI.py`) as a Gradio UI for user interaction.
1. **Defining MCP Tools and Resources (MCP Server):** functions decorated with `@mcp.tool()` or `@mcp.resource()` in the `MCPServer_HomeAutomation.py` file define callable actions (Tools) or retrievable data (Resources) for the AI.
``` Python
from mcp.server.fastmcp import FastMCP
mcp = FastMCP("Home Automation")
@mcp.tool()
def control_light(action: str) -> str: # Generalized snippet
# ...
return "Light controlled."
@mcp.resource("home://device_status")
def get_device_status() -> str: # Generalized snippet
# ...
return "{}"
if __name__ == "__main__":
mcp.run() # Starts the MCP server
```
2. **Establishing MCP Server Connection (MCP Client):** the `MCPClient_GradioUI.py` starts the server as a subprocess and connects using `MCPServerStdio` to enable tool discovery.
``` Python
import subprocess
from agents.mcp import MCPServerStdio
server_process = subprocess.Popen([...]) # Start server
mcp_server = MCPServerStdio(...)
await mcp_server.__aenter__() # Initialize connection
```
3. **Initialising AI Agent with MCP Servers (MCP Client):**
An `Agent` is initialised with the connected `mcp_servers`. The agent's instructions are dynamically updated based on MCP tool availability.
``` Python
from agents import Agent, OpenAIChatCompletionsModel
agent = Agent(
name="Home Assistant",
instructions="Use tools...",
model=OpenAIChatCompletionsModel(...),
mcp_servers=[mcp_server], # Link MCP server
)
```
4. **Processing User Input and Running Agent (MCP Client):** when a user inputs a query, `Runner.run()` is invoked. The AI model, aware of the MCP tools, decides whether to call a relevant tool or access a resource via the MCP layer to fulfill the request.
```Python
from agents import Runner
async def process_user_input(user_input, agent):
result = await Runner.run(starting_agent=agent, input=user_input)
return result.final_output
```
> [!NOTE]
> Hackster article about MCP can be found [here](https://www.hackster.io/news/ai-agentic-protocols-part-1-model-context-protocol-mcp-f9c7d198fe4c).
## Part 2: Agent2Agent Protocol (A2A)
> [!Caution]
> Work in progress. To be updated soon!
## Part 3: Agent Communication Protocol (ACP)
> [!Caution]
> Work in progress. To be updated soon!
## Demo videos on YouTube
- [AI Agentic protocols: Part 1 - MCP (Home Automation UI demo)](https://youtu.be/HG9CIeBkco4)