11.4 Protocol Ecosystem Intermediate Free

Prerequisites: 11.1 MCP, 11.2 A2A, 11.3 ANP

Why Do We Need It? (Problem)

Problem: How to choose among the three protocols?

After learning the previous three sections, you might have these questions:

❓ Are MCP, A2A, ANP competing protocols?
❓ Which protocol should I use for my project?
❓ Can I use multiple protocols together?
❓ Will these protocols replace each other?

Real Scenario: Selection Confusion

# Scenario 1: Simple weather query
"Claude, what's the weather in Beijing today?"
→ Which protocol to use?

# Scenario 2: Translation + proofreading workflow
text → Translation Agent → Proofreading Agent → output
→ Which protocol to use?

# Scenario 3: Enterprise-level Agent platform
1000 Agents, cross-department collaboration, needs load balancing and fault tolerance
→ Which protocol to use?

Answer: They're not competing, but complementary layers!

What Is It? (Concept)

Three-layer Protocol Model: Tool Layer → Agent Layer → Network Layer

Each layer solves different problems:

Layer	Protocol	Problem Solved	Analogy
Tool Layer	MCP	How AI calls external tools	USB protocol (connecting devices)
Agent Layer	A2A	How Agents collaborate	HTTP (webpage communication)
Network Layer	ANP	How large-scale Agents are managed	TCP/IP (internet foundation)

They are progressive, not replacement:

Detailed Comparison:

1. Capability Scope

MCP (Tool Layer):
┌──────────────────────────┐
│  Claude  →  Weather API   │
│  Claude  →  Database      │
│  Claude  →  File System   │
└──────────────────────────┘
Focus: Single AI calling multiple tools

A2A (Agent Layer):
┌──────────────────────────┐
│  Translation Agent → Proofreading Agent │
│  Research Agent → Writing Agent │
│  Analysis Agent → Visualization Agent│
└──────────────────────────┘
Focus: Collaboration between Agents

ANP (Network Layer):
┌──────────────────────────┐
│  Registry (Registration center) │
│  Router (Smart routing)   │
│  Load Balancer (Load balancing) │
│  Gateway (Unified entry)  │
└──────────────────────────┘
Focus: Large-scale Agent network management

2. Communication Mode

Dimension	MCP	A2A	ANP
Mode	Sync RPC	Async REST	Distributed messaging
State	Stateless	Task state	Agent state
Discovery	None (predefined)	Agent Card	Registry
Timeout	Short (seconds)	Medium (minutes)	Long (hours)

3. Use Cases

MCP is suitable for:

✅ Single AI application needs external capabilities ✅ Moderate number of tools (few to dozens) ✅ Synchronous calls, fast return ✅ Local or simple remote tools

Example:

# Claude Desktop calling local file system
"Please help me organize files on desktop"
→ MCP Server (filesystem) executes local operations

A2A is suitable for:

✅ Multiple Agents collaborating on complex tasks ✅ Medium number of Agents (few to dozens) ✅ Asynchronous tasks, possibly time-consuming ✅ Needs capability discovery and task status tracking

Example:

# Content creation pipeline
User input → Research Agent → Writing Agent → Editing Agent → Output
(Each Agent communicates via A2A protocol)

ANP is suitable for:

✅ Large-scale Agent network (hundreds/thousands) ✅ Cross-department, cross-organization collaboration ✅ Needs load balancing, fault tolerance, monitoring ✅ Enterprise-level platforms and ecosystems

Example:

# Enterprise Agent platform
- 100 data analysis Agents
- 50 customer service Agents
- 30 R&D Agents
→ ANP provides unified management, routing, monitoring

4. Protocol Stack Examples

Scenario 1: Simple Query (MCP only)

User: "What's the weather in Beijing?"
 ↓
Claude (LLM)
 ↓
MCP Client
 ↓
MCP Server (Weather API)
 ↓
External weather service

Scenario 2: Multi-Agent Collaboration (MCP + A2A)

User: "Write a blog post about RAG technology"
 ↓
Coordinator
 ↓
A2A → Research Agent
       ↓
       MCP → Search engine, database
 ↓
A2A → Writing Agent
       ↓
       MCP → Grammar checker
 ↓
A2A → Editing Agent
 ↓
Final output

Scenario 3: Enterprise Platform (MCP + A2A + ANP)

User: "Analyze this quarter's sales data"
 ↓
ANP Gateway (Unified entry)
 ↓
ANP Router (Smart routing)
 ↓
ANP Registry (Find data analysis Agent)
 ↓
ANP Load Balancer (Select low-load Agent)
 ↓
Data Analysis Agent (collaborates via A2A)
  ├→ Data Cleaning Agent
  │   └→ MCP → Database
  ├→ Statistical Analysis Agent
  │   └→ MCP → Python execution environment
  └→ Visualization Agent
      └→ MCP → Chart generation tool
 ↓
Result aggregation
 ↓
ANP Gateway returns to user

Three-layer Protocol Interaction:

Ecosystem Status (February 2026):

Protocol	Maturity	Ecosystem	Adoption
MCP	⭐⭐⭐⭐⭐ Mature	Claude Desktop, Cursor, Windsurf native support	Widely used
A2A	⭐⭐⭐ Early	Google released, community implementing	Small-scale trials
ANP	⭐⭐ Proposal	IBM research project, specification under discussion	Concept stage

Hands-on Practice (Practice)

Decision Flowchart: Which Protocol Should I Use?

Real Case Comparison:

Application Scenario	Recommended Protocol	Reason
Personal AI assistant	MCP	Single AI, calls calendar, email, files
Code review tool	MCP	Single AI, calls Git, Linter, Test Runner
Content creation platform	A2A	Research, writing, editing Agents collaborate
Customer service system	A2A	Triage, order, logistics specialist Agents collaborate
Enterprise AI platform	ANP + A2A + MCP	Large-scale Agents, need unified management and routing
AI Agent marketplace	ANP + A2A	Third-party Agent interconnection, needs registration and discovery

Practical Recommendations:

# 1. Start with MCP
# Suitable for: Personal projects, small teams, rapid prototyping

from mcp.server import Server

app = Server("my-tool")
@app.list_tools()
async def list_tools():
    return [Tool(name="search", ...)]

# 2. Expand to A2A
# Suitable for: Multi-Agent collaboration, medium-sized projects

class ResearchAgent:
    def get_card(self):
        return {"name": "researcher", "capabilities": [...]}
    
    def execute_task(self, task):
        # Call other Agents
        result = call_a2a_agent("writer_agent", task)

# 3. Evolve to ANP
# Suitable for: Large-scale, enterprise-level, cross-organization

class ANPPlatform:
    def __init__(self):
        self.registry = AgentRegistry()
        self.router = SmartRouter(self.registry)
        self.gateway = APIGateway(self.router)

Complete example in Notebook:

Run locally: jupyter notebook demos/11-protocols/protocols_overview.ipynb

Summary (Reflection)

• What's solved: Understood the positioning and selection logic of MCP, A2A, ANP protocols
• What's not solved: Now we know protocols, but how to build AI systems with "memory"? — Next chapter introduces RAG and memory storage
• Key Takeaways:
  1. Three-layer model: MCP (Tool Layer) → A2A (Agent Layer) → ANP (Network Layer)
  2. Not competing, but complementary: Each layer solves problems at different scales and scenarios
  3. Selection principle: Start from requirements, don't over-engineer
  4. Can be mixed: Large projects can use multiple protocols simultaneously
  5. Different maturity: MCP mature, A2A early stage, ANP proposal stage
  6. Start small and grow: Start with MCP, expand to A2A when needed, consider ANP for enterprise-level

Selection Quick Reference:

Scenario                     → Protocol
─────────────────────────────────
Personal AI assistant        → MCP
Code generation tool         → MCP
Content creation platform    → A2A
Customer service system      → A2A
Data analysis pipeline       → A2A
Enterprise Agent platform    → ANP + A2A + MCP
AI Agent marketplace         → ANP + A2A

Next Step:

Chapter 11 completed the "Protocol" section, understanding interconnection between AI systems. But there's still a key question:

How does AI remember information? How to retrieve relevant knowledge?

→ Chapter 12: RAG & Memory Storage

---

Last updated: 2026-02-20