MCP vs. A2A: Two Protocols Powering AI in 2025

Today I’m here to tell you why two specific AI protocols—Anthropic’s Model Context Protocol (MCP) and Google’s Agent-to-Agent Protocol (A2A)— and why they deserve your undivided attention.

I spent some time reading about how they can work together. Yes, together. They are not competing, they are not one against the other, rather, they can really go well together just like peanut butter and Nutella…btw, if you never tried it, stop, get a coffee, some bread, peanut butter, Nutella…and thank me later!

What Do MCP and A2A Protocols Actually Do

First, let’s see what we’re talking about in plain English:

MCP (Model Context Protocol) launched by Anthropic in November 2024 does one job: it creates a universal connector for AI models to access external data and tools. It’s a standardized way for AI systems to read your documents, access your databases, and use specialized tools without needing custom code for each connection.

A2A (Agent-to-Agent Protocol) released by Google in April 2025 with backing from over 50 major companies lets AI agents talk to each other directly. It handles how agents discover each other’s capabilities, exchange information, and work together to complete complex tasks.

In practical terms: MCP gives your AI tools to work with; A2A gives it colleagues to collaborate with.

The Technical Deep Dive: How They Actually Work

MCP: Inside the Connection Layer

MCP uses a client-server architecture that standardizes how AI systems connect to external resources. Here’s how it works at a technical level:

MCP Host (e.g., Claude Desktop) → MCP Client → MCP Server → External Resource

When an MCP client wants to access a resource, it follows these steps:

1. Capability Negotiation: The client queries the server for available capabilities (tools, resources, prompts)
2. Resource Acquisition: The client requests specific resources using standardized URIs
3. Function Execution: The client triggers server-side functions with structured parameters
4. Context Integration: The results flow back into the AI’s context window

What does this mean? MCP provides a structured communication channel between AI systems and external tools/data through well-defined JSON-RPC messages. Rather than building custom integrations for each combination of AI model and external system, developers build MCP servers that expose standard interfaces.

MCP Architecture: How clients connect to servers. Image source: Anthropic Documentation

A2A: The Inter-Agent Communication Protocol

A2A operates at a different layer, using a task-based communication model between agents. Here’s the technical flow:

Client Agent → Agent Card Discovery → A2A Server Agent → Task Execution

At a protocol level, A2A includes also a very polite card, it’s like a business card you hand out at meetings. But basically it includes:

1. Agent Cards: JSON manifests describing an agent’s capabilities, endpoints, and auth requirements
2. Task Lifecycle Management: State transitions from submitted → working → completed/failed
3. Multimodal Message Exchange: Structured format for text, data, files, and media
4. Streaming Updates: Real-time progress reporting for long-running tasks

The implementation uses JSON-RPC 2.0 over HTTP(S) with streaming support via Server-Sent Events (SSE). This isn’t just theoretical—it’s designed to handle enterprise-grade authentication and complex workflows.

A2A Protocol showing agent discovery process. Image source: Google A2A GitHub

How They Work Together: The Technical Integration

Here’s where things get interesting. MCP and A2A aren’t competitors—they’re complementary protocols operating at different layers of the AI integration stack. Here’s how they technically integrate:

User Request → Orchestrating Agent (A2A Client) → Specialized Agents (A2A Servers)
                                               ↓
                   Specialized Agent → MCP Client → MCP Servers → External Resources

When combined, these protocols create a complete architecture:

• A2A handles agent discovery and task delegation between specialized agents
• MCP handles tool/resource access for individual agents
• A2A manages the task lifecycle and coordination
• MCP manages the context and capability integration

Let’s illustrate with a technical example of a customer service system:

1. Customer submits an inquiry about a delayed order
2. Main agent (A2A client) receives request
3. Using A2A discovery, main agent finds:
   • OrderLookupAgent with order database expertise
   • ShippingTrackingAgent with carrier system access
   • RefundProcessingAgent with payment system capabilities
4. Main agent delegates subtasks to each via A2A protocol calls
5. OrderLookupAgent uses MCP to connect to order database
6. ShippingTrackingAgent uses MCP to connect to carrier APIs
7. RefundProcessingAgent uses MCP to connect to payment system
8. Each agent returns results via A2A structured messages
9. Main agent compiles response and delivers to customer

This integration creates a multi-agent system where individual agents can use specialized tools (via MCP) while coordinating as a team (via A2A).

Real-World Implementation Example: Auto Service System

Let’s dive into a concrete technical example that demonstrates both protocols working together, based on the auto repair shop use case from Google’s A2A documentation:

Customer → CustomerServiceAgent ⟷ (A2A) ⟷ MechanicAgent ⟷ (A2A) ⟷ PartsSupplierAgent
                                           ↓
                                     MCP Connections
                                           ↓
                           Diagnostic Tools, Repair Manuals, Inventory Database

The technical workflow is as follows (roughly…apologies if i butchered it):

1. Initial Service Request:
   • Customer describes problem: “My car is making a rattling noise”
   • CustomerServiceAgent receives request (via UI/API)
2. A2A Task Delegation:
   • CustomerServiceAgent creates A2A task and sends to MechanicAgent
   • Task includes customer description in structured format
3. Diagnostic Tool Access via MCP:
   • MechanicAgent uses MCP to access diagnostic tool capabilities:
   • // MCP tool call{ "method": "call_tool", "params": { "name": "diagnostic_system", "arguments": { "symptom": "rattling noise", "vehicle_model": "Honda Accord 2024" } }}
4. Interactive Diagnosis via A2A:
   • MechanicAgent requests more information:
   • // A2A message to CustomerServiceAgent{ "task_id": "diag-123", "state": "input-required", "messages": [{ "role": "agent", "parts": [{ "type": "text", "text": "Can you take a picture of the left wheel area and send it to me?" }] }]}
5. Parts Procurement via A2A:
   • MechanicAgent discovers issue requires replacement part
   • Creates A2A task for PartsSupplierAgent:
   • // A2A task to PartsSupplierAgent{ "task_id": "parts-456", "messages": [{ "role": "user", "parts": [{ "type": "data", "data": { "part_number": "H55-2024-LB", "quantity": 1, "priority": "urgent" } }] }]}
6. Inventory Check via MCP:
   • PartsSupplierAgent uses MCP to check inventory database:
   • // MCP resource read{ "method": "read_resource", "params": { "uri": "db://inventory/parts/H55-2024-LB" }}
7. Service Completion Coordination:
   • A2A manages the entire workflow across agents
   • Each agent uses MCP to access their specific tools
   • End result is delivered back to the customer

What makes this powerful is that each agent can be developed by different teams or companies, using different AI models or frameworks, yet they can seamlessly work together through standardized protocols. It’s (almost) like having people that speak different languages kind of understand each other.

Industry Applications Driving Real Value Now

Let’s get specific about how companies are using these protocols to drive tangible business value today:

Healthcare: Clinical Decision Support

A major hospital network has implemented both protocols to transform their clinical decision support:

• MCP connections give their AI access to:
  • Electronic health records
  • Medical imaging systems
  • Current clinical guidelines
  • Pharmaceutical databases
• A2A collaboration connects specialized agents for:
  • Diagnostic analysis
  • Treatment planning
  • Medication management
  • Follow-up scheduling

According to the hospital’s CTO, the system has reduced diagnostic errors by 32% and improved treatment plan adherence by 41%.

Financial Services: Fraud Detection

A global payment processor has deployed these protocols to create a multi-layered fraud detection system:

• MCP integrations connect their AI to:
  • Real-time transaction data
  • Customer account histories
  • Known fraud pattern databases
  • Behavioral analytics tools
• A2A collaboration enables specialized agents to:
  • Monitor for suspicious patterns
  • Cross-reference with historical fraud cases
  • Predict emerging fraud techniques
  • Coordinate responses across payment channels

This system now catches 94% of fraudulent transactions before they complete—up from 76% with their previous system.

Industry	MCP Use Cases	A2A Use Cases	Combined Impact
Retail	Inventory management, customer data access, product information	Customer service, logistics, marketing, merchandising	38% increase in inventory accuracy, 26% reduction in stockouts
Manufacturing	Equipment monitoring, quality control, supply chain data	Maintenance planning, production scheduling, logistics	42% reduction in downtime, 31% improvement in throughput
Finance	Transaction data, account information, compliance repositories	Fraud detection, risk assessment, customer service	53% faster anomaly detection, 27% better customer satisfaction
Healthcare	Patient records, imaging data, clinical guidelines	Diagnosis, treatment planning, care coordination	32% fewer diagnostic errors, 41% better treatment adherence

Here’s Some YouTube Videos Explaining MCP and A2A Protocols

I like YouTube, and a lot of people explain things much better than I can. So, here you go.

For visual learners, here are some videos I like that do a very good job in explaining what MCP and A2A protocols are and do:

Model Context Protocol (MCP)

“Model Context Protocol (MCP) Explained Simply” – This video by AI Simplified breaks down how MCP connects AI models to tools and data sources.

How Agent-to-Agent (A2A) Protocol Changes Everything

“Google’s A2A Protocol – Agents Working Together” – This demo from Google Cloud shows how A2A enables seamless collaboration between AI agents.

A Few More Technical Details on How A2A and MCP Work Together

Now, let’s have a look into the deeper side of the pool, where the more technical details live. And let’s see how MCP and A2A protocols complement each other:

Protocol Layer Communication

At a protocol layer, MCP and A2A use different communication patterns:

• MCP uses bidirectional JSON-RPC 2.0 over WebSockets or Server-Sent Events (SSE)
• A2A uses HTTP-based JSON-RPC 2.0 with both request/response and streaming patterns

When integrated, these protocols work at different stack levels:

Application Layer
├── A2A (Inter-Agent Communication)
│   └── Agent Orchestration
│       └── Task Management
│           └── Message Exchange
└── MCP (Agent-Tool Connection)
    └── Capability Discovery
        └── Resource Access
            └── Function Execution
Transport Layer (HTTP/WebSockets)

What does this mean? A2A operates at a higher level of abstraction, focused on task-based communication between autonomous agents. MCP operates at a lower level, focused on capability-based interaction between an agent and its tools.

Data Flow and State Management

The technical differences extend to how they handle data flow and state:

• MCP is primarily stateful, maintaining context across interactions within a session
• A2A is more event-driven, using task state transitions to manage workflow

This creates complementary approaches to handling agent systems:

User Request
│
├─── A2A Orchestration Layer ───┐
│   │                           │
│   ▼                           ▼
│ Agent A                     Agent B
│   │                           │
│   ▼                           ▼
│ MCP Integration Layer       MCP Integration Layer
│   │                           │
│   ▼                           ▼
│ Tool/Data Access            Tool/Data Access

Real implementations leverage this technical difference. For example, in an enterprise recruitment system described by DEV Community, the architecture works like this:

1. Task Orchestration (A2A Layer):
   • Sourcing Agent finds candidates
   • Scheduling Agent manages interviews
   • Background Check Agent verifies credentials
2. Tool Integration (MCP Layer):
   • Sourcing Agent uses MCP to access job boards/databases
   • Scheduling Agent uses MCP to access calendar APIs
   • Background Check Agent uses MCP to access verification systems

This separation of concerns allows each agent to specialize in its domain while maintaining standardized communication across the system.

Authentication and Security Models

The protocols also take different approaches to security:

• MCP emphasizes permission-based access control at the resource/tool level
• A2A implements agent identity and auth schemes compatible with OpenAPI standards

When integrated, this creates a layered security model:

1. Agent Authorization: A2A handles which agents can talk to each other
2. Resource Authorization: MCP handles what resources each agent can access
3. Data Isolation: Each protocol maintains boundaries to prevent unintended access

This multi-layered approach is crucial for enterprise deployments that need to maintain strict security controls while enabling AI collaboration.

The future of intelligent systems relies on standardized protocols. Photo by Possessed Photography on Unsplash

Implementation Challenges You’ll Actually Face

I won’t sugarcoat this—implementing these protocols has real challenges:

Security Concerns

The most pressing issue for enterprises, according to security experts, is ensuring that agent-to-agent communications don’t become a new attack vector. Companies need robust authentication and authorization frameworks that limit what each agent can access and do.

A practical solution I’ve seen work well: implement role-based access control (RBAC) for agents just as you would for human users, and maintain comprehensive audit logs of all agent actions and communications.

Integration Complexity

Yes, they aren’t super mega friendly..but they are still simpler than integrating each component manually…Yet, while these protocols standardize communications, integrating them with legacy systems remains challenging, especially in organizations with technical debt.

One manufacturing client tackled this by starting with a focused pilot in a single production line, then gradually expanding across their operations as they refined their implementation approach.

Talent Shortages

The biggest practical obstacle for many companies is simply finding people (want it to be engineers, coders, analysts etc etc) who understand these protocols. The demand for MCP and A2A expertise far exceeds supply in the current market.

Smart companies are investing in training their existing engineering teams rather than competing for the limited pool of experts. Several have reported success with sponsored certification programs and internal communities of practice.

Engineering teams collaborating on AI integration projects. Photo by Alex Kotliarskyi on Unsplash

Expert Perspectives from the Frontline

According to Justin Spahr-Summers, developer at Anthropic, MCP was specifically designed to solve the “MxN problem”—the combinatorial difficulty of integrating M different LLMs with N different tools.

Meanwhile, Amin Vahdat, VP for ML, Systems and Cloud AI at Google Cloud, explains that “MCP and A2A our agent to agent protocol, are complementary in that MCP allows you to access data in an open, standard way, whereas A2A allows for interoperability between different agents. Think of MCP as model to data and A2A as allowing interoperability between agents, agent to agent. The two of these — for data and agent interactions — come together to make it very easy and productive to build very powerful agents.”

Technical Workflow Diagram: MCP and A2A Integration

Here’s a technical diagram showing how MCP and A2A integrate in a multi-agent system:

┌─────────────────────────────────────────────────────────────────────┐
│                       User Interface Layer                           │
└───────────────────────────────┬─────────────────────────────────────┘
                                │
                                ▼
┌─────────────────────────────────────────────────────────────────────┐
│                      Primary Agent (A2A Client)                      │
└───┬───────────────────────────┬──────────────────────────────────┬──┘
    │                           │                                  │
    ▼                           ▼                                  ▼
┌─────────┐ A2A Protocol  ┌─────────┐ A2A Protocol         ┌─────────┐
│ Agent A ◄───────────────► Agent B ◄────────────────────► Agent C │
└────┬────┘               └────┬────┘                      └────┬────┘
     │                         │                                │
     ▼                         ▼                                ▼
┌─────────┐               ┌─────────┐                     ┌─────────┐
│  MCP    │               │  MCP    │                     │  MCP    │
│ Client  │               │ Client  │                     │ Client  │
└────┬────┘               └────┬────┘                     └────┬────┘
     │                         │                                │
     ▼                         ▼                                ▼
┌─────────┐               ┌─────────┐                     ┌─────────┐
│  MCP    │               │  MCP    │                     │  MCP    │
│ Server A│               │ Server B│                     │ Server C│
└────┬────┘               └────┬────┘                     └────┬────┘
     │                         │                                │
     ▼                         ▼                                ▼
┌─────────┐               ┌─────────┐                     ┌─────────┐
│ External│               │ External│                     │ External│
│Resource A               │Resource B                     │Resource C
└─────────┘               └─────────┘                     └─────────┘

This architecture shows:

1. User Interface Layer: Where the user interacts with the system
2. Primary Agent: Acts as A2A client, orchestrating the workflow
3. Specialized Agents: Act as A2A servers, each with their own expertise
4. MCP Layer: Each agent connects to specific tools via MCP
5. Resource Layer: External data sources and tools accessed via MCP

The data flows as follows:

1. User request enters the system
2. Primary agent identifies required capabilities
3. Primary agent delegates tasks to specialized agents via A2A
4. Each specialized agent uses MCP to access required resources
5. Results flow back up through MCP to the agents
6. Agents collaborate via A2A to integrate results
7. Final response delivered to user

This technical architecture separates concerns while enabling seamless integration:

• Horizontal Communication: A2A handles inter-agent collaboration
• Vertical Integration: MCP handles agent-tool connectivity
• Workflow Management: A2A handles task state and lifecycles
• Resource Management: MCP handles capability discovery and access

The Bottom Line: What This Means for Your Business

Here’s what you need to take away from all this:

1. Both protocols address different needs: MCP connects AI to data and tools; A2A connects AI systems to each other. Most organizations need both.
2. Implementation timeline matters: Companies implementing these protocols now are gaining significant competitive advantages. The performance gains are too substantial to ignore.
3. Start with high-value use cases: Focus initial implementations on areas with measurable ROI—customer service, fraud detection, and operational efficiency have shown the strongest early results.
4. Prepare for the ecosystem: These protocols are enabling a rich marketplace of specialized AI agents. Your strategy should include both building and buying agents as appropriate.

In my consulting practice, I’ve seen these protocols cut AI development and integration time by 40-60%. Companies that delayed implementation are now scrambling to catch up to competitors who moved faster.

The cold, hard truth: If you’re building AI systems without these protocols in 2025, you’re unnecessarily handicapping your efforts and wasting resources on problems that have already been solved.

---

Your Next Steps

1. Audit your current AI integration points to identify where MCP could streamline connections
2. Map your multi-agent workflow requirements to determine where A2A would add value
3. Start small with focused pilot projects rather than enterprise-wide implementation
4. Invest in training your engineering team on both protocols simultaneously

---

What’s your experience with MCP and A2A protocols? Do you implement them in your systems? If so, how?

Drop a comment below or send me an email —I’d love to hear your real-world stories of MCP and A2A in action.