A2A protocol demonstration with MCP Protocol
🤖 I Built an Interactive A2A + MCP Agent Library — Here's What I Learned
The next frontier in AI isn't smarter single models. It's smarter agent networks — and two protocols are quietly becoming the backbone of that shift: A2A (Agent-to-Agent) and MCP (Model Context Protocol).
I recently built an open-source interactive demo that brings both protocols to life in a single browser-based playground. Here's what I discovered.
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🔷 THE PROBLEM WITH SOLO AGENTS
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Most AI demos show a single agent handling a task from start to finish. But real-world complexity doesn't work that way. A "research and publish a competitive analysis" task might require:
→ A planner that understands the big picture
→ A researcher that knows how to query the web
→ An analyst that can run statistical models
→ A writer that produces coherent prose
→ A reviewer that catches errors before delivery
No single model should do all of this alone. The question is: how do multiple agents communicate, delegate, and collaborate without chaos?
That's exactly what A2A and MCP solve — at different layers.
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🔷 WHAT IS A2A PROTOCOL?
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A2A (Agent-to-Agent) is a communication standard that defines how autonomous AI agents talk to each other across boundaries — even across different vendors, frameworks, or deployment environments.
Think of it as the HTTP of the agentic web.
The core message types:
📨 TaskRequest — "Hey ResearchAgent, please gather sources on quantum computing by 11am."
📬 TaskResult — "Done. Here are 12 sources with confidence score 0.94."
🔀 AgentHandoff — "I've completed my part. Passing context to ReviewAgent."
What makes A2A powerful is that it's asynchronous, identity-aware, and context-preserving. An orchestrator can spin up 5 workers in parallel, track their progress, handle failures gracefully, and aggregate results — all via structured JSON messages.
In my demo, the OrchestratorAgent decomposes a user's task into subtasks, assigns them to workers like ResearchAgent and AnalysisAgent, and then watches real-time A2A messages flow across the network. You can click any message and inspect its full payload.
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🔷 WHAT IS MCP PROTOCOL?
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MCP (Model Context Protocol) operates at a different layer. Where A2A handles agent-to-agent communication, MCP handles agent-to-tool communication.
It's a JSON-RPC 2.0 standard that lets any AI model discover and invoke external tools — web search, code execution, file I/O, databases, APIs — in a vendor-neutral way.
The key messages:
🔧 tools/list — "What tools are available to me?"
⚡ tools/call — "Run web_search with query='quantum computing 2026', limit=5"
✅ Tool Result — Structured response with content, error flag, and metadata
MCP is to AI tools what npm is to JavaScript packages. It creates a shared ecosystem where a tool built once — say, a vector_search connector — can be used by any MCP-compatible agent, regardless of which company built it.
In the demo, you can browse the full tool registry (web_search, code_execute, file_read, vector_search, shell_exec, and more), inspect their JSON schemas, and simulate live tool calls.
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🔷 HOW THEY WORK TOGETHER
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Here's where it gets interesting. A2A and MCP aren't competing — they're complementary layers in a full agent stack:
┌──────────────────────────────────┐
│ User / Application │
├──────────────────────────────────┤
│ A2A Layer — Agent Coordination │
│ (TaskRequest, Handoff, Result) │
├──────────────────────────────────┤
│ MCP Layer — Tool Invocation │
│ (tools/call, tools/list, JSON) │
├──────────────────────────────────┤
│ External Tools & Data Sources │
└──────────────────────────────────┘
A typical flow looks like this:
1. User submits: "Analyze market data and write a competitor report"
2. OrchestratorAgent (A2A) → splits into 3 subtasks → sends TaskRequests
3. AnalysisAgent receives TaskRequest → calls MCP tool: code_execute (runs Python stats model)
4. ResearchAgent → calls MCP tool: web_search (finds competitor data)
5. WriterAgent → calls MCP tool: file_write (saves report draft)
6. WriterAgent → A2A HandoffMessage → ReviewAgent ("Please review draft")
7. ReviewAgent completes → sends TaskResult back up the chain
8. OrchestratorAgent aggregates and returns final output
Eight distinct protocol interactions. Two standards. Zero hallucinated tool calls.
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🔷 WHAT THE DEMO INCLUDES
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The AgentLib playground is a fully interactive, single-file HTML app with:
✦ Live Agent Network canvas — animated particle flows showing real-time message routing
✦ Message Bus — every A2A and MCP message logged with full JSON inspection
✦ Agent Registry — register, configure, activate/deactivate agents with A2A identity cards
✦ MCP Tool Registry — browse tools by category, view schemas, simulate tool calls
✦ Protocol Reference — side-by-side spec for all message types with real examples
✦ Preset workflows — Research, Analyze, Write & Review, DevOps scenarios
All client-side. No backend. No API keys. Just open the file.
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🔷 WHY THIS MATTERS RIGHT NOW
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We're at an inflection point. The race isn't just to build better models — it's to build better infrastructure for those models to collaborate.
A2A and MCP represent the early consensus forming around that infrastructure. Google, Anthropic, and others are already shipping products that leverage these patterns. The developers who understand how these protocols compose together today will have a significant head start building the agent pipelines of tomorrow.
If you're building anything in the AI agent space — whether it's multi-agent orchestration, RAG pipelines, AI-powered automation, or developer tooling — I'd encourage you to dig into both specs.
The agent web is being built right now. The protocols are the foundation.
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#AIAgents #A2AProtocol #ModelContextProtocol #MCP #LLM #GenerativeAI #AgenticAI #SoftwareEngineering #ArtificialIntelligence #OpenSource