🤝 A2A Protocol Tester + Chat App Please restart your A2A server on your trial Mule instance if you see an error.
What is A2A (Agent-to-Agent Protocol)?
A2A is a standardized protocol that allows AI agents to discover and communicate with each other automatically.
Think of it like HTTP for the web, but for AI agents. Instead of humans clicking links, agents use A2A to
find other agents, understand their capabilities, and exchange information seamlessly. Please do not include forward slash (/) at the end of the URL.
📇 Discover the Agent (Agent Card)
Put in the link to the A2A server deployed on Mule Cloudhub, then fetch its Agent Card:
📋 What the Agent Card Tells Us:
⚠️ What the Agent Card DOESN'T Tell Us:
Example queries - What's a valid question? "What is component 4417-K?" or "Get status of 4417-K" or something else?
Data scope - Which components are in the database? What fields are available?
Response format details - Will answers be structured data or natural language?
Rate limits - How many requests can we make?
Error scenarios - What happens if a component doesn't exist?
🎯 Observations: Agent Card provides metadata, but developers still control how descriptive to be. More detailed descriptions = easier integration. Apart from simplifying integrations - the card is very helpful in facilitating orchestrations since an agent broker can use the card information for semantic routing.
💬 Ask Your A2A Agent Please wait for a few seconds before asking this question. Restart your A2A server on your trial Mule instance if you see an error.
⏳ Sending A2A request using curl...
📤 What We Sent (A2A Request)
🔍 What Makes This A2A-Compliant?
JSON-RPC 2.0 Wrapper: A2A uses JSON-RPC as its transport layer. The "jsonrpc": "2.0" field indicates this is a JSON-RPC 2.0 request.
Standardized Method:"method": "message/send" is the A2A protocol's standard method for sending messages to agents.
Message Structure: The "params" contains a "message" with required fields:
"kind": "message" - Simple message (vs "task" for long operations)
"role": "user" - Who's sending
"parts" - Array of content (text, images, etc.)
Unique IDs:id, messageId, and contextId for tracking and conversation flow.
📥 What We Received (A2A Response)
🎯 A2A Response Structure
JSON-RPC Result:"result" contains the successful response.
Agent Role:"role": "agent" indicates this is from the AI agent.
Message Kind:"kind": "message" for simple responses.
Parts Array: The answer is in "parts" with "kind": "text".
Tracking:messageId and taskId for context.
💬 Plain English Answer
🌟 Why A2A Matters
Before A2A: Every AI agent had its own custom API format. Connecting Agent A to Agent B required writing custom integration code. Connecting 10 different agents meant 45 different integrations!
With A2A: All agents speak the same standardized protocol. Benefits:
Discoverability: Agents publish an "Agent Card" (like a business card) describing their capabilities
Interoperability: Any A2A client can talk to any A2A agent without custom code
Orchestration: Agent Brokers can coordinate complex multi-agent workflows automatically
Flexibility: Switch agents without rewriting your application
Real-World Example - This Agent:
MCP (Model Context Protocol): Connects to an engineering database to fetch live component data
Groq AI (llama-3.3-70b): Intelligently processes questions and summarizes database results
A2A Protocol: Exposes this combined capability in a standard, interoperable format
💡 Key Insight: As a caller, you don't need to know about MCP, Groq, or databases. You send a standard A2A message and get a standard A2A response. That's the power of protocol standardization!
Analogy: A2A is to AI agents what HTTP is to websites. Before HTTP, every online service had its own protocol. HTTP standardized web communication and enabled the internet explosion. A2A aims to do the same for AI agents!
📋 Behind the Scenes: How This Works
1️⃣ Your Question → Typed in the form above 2️⃣ A2A Packaging → Wrapped in JSON-RPC with all required A2A fields 3️⃣ Transport → Sent via HTTPS POST to your MuleSoft agent on CloudHub 4️⃣ Agent Receives → Validates against A2A specification 5️⃣ Tool Selection → Groq AI decides if MCP tool is needed 6️⃣ Data Retrieval → MCP queries the engineering database 7️⃣ AI Processing → Groq summarizes the data into natural language 8️⃣ A2A Response → Packaged back into A2A format 9️⃣ Display → Shown here with both raw JSON and plain English
🔑 Architecture Insight: Steps 5-7 are completely invisible to you. You only interact with the standard A2A interface. The agent could use completely different tools tomorrow (different database, different AI) and your code wouldn't need to change!