Practical Use Cases: When to Choose MCP vs A2A

Real-world scenarios and implementation examples to help you make the right protocol choice for your AI application

Enterprise Knowledge Management

Examining protocol choices for building an enterprise document assistant that processes company information

Enterprise Document Assistant

Building an AI system to query and process corporate knowledge bases

Scenario

A large corporation needs to build an internal AI assistant that can answer employee questions by retrieving information from multiple document repositories, including HR policies, technical documentation, and project wikis.

MCP Protocol

Primary Choice: MCP is ideal for this scenario because it focuses on structured context management when querying large language models.

Implementation Approach

The system would use MCP to:

  • Structure document chunks retrieved from various repositories
  • Maintain conversation history with proper metadata
  • Include user role and access permissions as context
  • Pass document reference information for citation
┌─────────────────┐  1. Query   ┌─────────────────┐
│                 │──────────▶  │                 │
│     Employee    │             │  RAG Processor  │
│                 │◀──────────  │                 │
└─────────────────┘  5. Answer  └─────────────────┘
                                        │ 
                              2. Retrieve Documents
                                        ▼
                              ┌─────────────────┐
                              │   Document      │
                              │  Repositories   │
                              └─────────────────┘
                                        │
                              3. Structure with MCP
                                        ▼
                              ┌─────────────────┐
                              │   LLM Service   │
                              │    (with MCP)   │
                              └─────────────────┘
                                        │
                                        │ 4. Response
                                        ▼

Benefits of Using MCP Here

  • Improved Context Quality: Properly structured document snippets with source metadata
  • Better Citations: Clear traceability of where information comes from
  • Reduced Token Usage: By structuring context efficiently
  • Enhanced Security: Metadata can include permission checks
  • Conversation Consistency: Maintaining structured dialogue history

When A2A Might Also Be Considered

  • If the document processing requires specialized agents (like a legal document agent, a code analysis agent, etc.)
  • If the system needs to interface with other organizational AI services
  • For complex workflows that involve multiple independent steps

Personal Assistant Ecosystem

Analyzing how to build a personal AI assistant that coordinates across multiple services

Multi-Service Personal Assistant

Creating an AI system that coordinates tasks across multiple domains and services

Scenario

A user wants a personal AI assistant that can coordinate complex tasks involving multiple services, such as planning a trip that requires flight booking, hotel reservations, creating an itinerary, and scheduling meetings with local contacts.

A2A Protocol

Primary Choice: A2A is ideal for this scenario because it enables coordination between specialized agents, each handling different aspects of the task.

Implementation Approach

The system would use A2A to:

  • Coordinate between specialized travel, scheduling, and communication agents
  • Allow each agent to focus on its domain expertise
  • Maintain task state across multiple service interactions
  • Enable dynamic discovery of available capabilities
┌─────────────────┐             ┌─────────────────┐
│                 │  1. Request │                 │
│      User       │──────────▶  │ Primary Personal│
│                 │◀──────────  │     Assistant   │
└─────────────────┘  8. Results └─────────────────┘
                                   │    ▲    ▲
                                   │    │    │
                     ┌─────────────┼────┼────┘
                     │             │    │
                     ▼             ▼    │
           ┌─────────────────┐ ┌─────────────────┐
           │                 │ │                 │
           │  Flight Booking │ │ Hotel Booking   │
           │  Agent (A2A)    │ │ Agent (A2A)     │
           └─────────────────┘ └─────────────────┘
                     │                  │
                     ▼                  ▼
           ┌─────────────────┐ ┌─────────────────┐
           │  Airline APIs   │ │  Hotel APIs     │
           └─────────────────┘ └─────────────────┘

Benefits of Using A2A Here

  • Task Decomposition: Breaking complex requests into manageable subtasks
  • Specialized Expertise: Each agent can focus on what it does best
  • Service Integration: Seamless incorporation of third-party services
  • Extensibility: New capabilities can be added by introducing new agents
  • Resource Efficiency: Only relevant agents are invoked for each task

When MCP Might Also Be Needed

  • For each individual agent when it needs to query an LLM for decision-making
  • When processing complex contextual information like user preferences
  • For maintaining conversation coherence within each agent's domain

IoT and Smart Home Ecosystem

Building an AI system that manages multiple smart devices while maintaining context

Smart Home Orchestration

Creating an AI system that coordinates across multiple smart devices while understanding user context

Scenario

A smart home system needs to coordinate multiple devices (lights, thermostat, entertainment system, security) while understanding complex user requests and maintaining awareness of household preferences and patterns.

Hybrid Approach

Primary Choice: This scenario benefits from combining both protocols - A2A for device coordination and MCP for contextual understanding.

Implementation Approach

The system would use:

  • A2A: For communication between device-specific agents (lighting agent, climate agent, security agent, etc.)
  • MCP: For central hub's interaction with LLM to understand user intent and maintain household context
┌─────────────────┐             ┌─────────────────┐
│                 │  Request    │   Central Hub   │
│  Homeowner      │──────────▶  │   (Uses MCP     │
│                 │◀──────────  │   for context)  │
└─────────────────┘  Response   └─────────────────┘
                                 │      │       │
                           A2A   │      │       │   A2A
                       Protocol  │      │       │  Protocol
                                 ▼      ▼       ▼
                     ┌─────────────┐ ┌─────────────┐ ┌─────────────┐
                     │  Lighting   │ │ Temperature │ │  Security   │
                     │   Agent     │ │   Agent     │ │   Agent     │
                     └─────────────┘ └─────────────┘ └─────────────┘
                           │              │               │
                           ▼              ▼               ▼
                     ┌─────────────┐ ┌─────────────┐ ┌─────────────┐
                     │  Smart      │ │  Smart      │ │  Security   │
                     │  Lights     │ │ Thermostat  │ │  System     │
                     └─────────────┘ └─────────────┘ └─────────────┘

Benefits of This Hybrid Approach

  • Device Coordination: A2A enables seamless interaction between device agents
  • Rich Context Understanding: MCP helps the central hub understand complex user requests
  • Preference Awareness: Historical context and user preferences maintained via MCP
  • Extensibility: New devices can be added without changing the core architecture
  • Multi-user Support: Context can include different household member preferences

Implementation Challenges

  • Synchronizing state between the central context and distributed agents
  • Managing complex security considerations across multiple systems
  • Balancing local processing vs. cloud-based interactions
  • Handling conflicts between automated routines and immediate commands

Protocol Decision Guide

Quick Decision Flowchart

                         Is your primary need coordinating 
                         multiple specialized AI services?
                                     │
                      ┌─────────────┴─────────────┐
                      │                           │
                      ▼                           ▼
                     YES                          NO
                      │                           │
                      │                           │
                      ▼                           ▼
        Are these services independently    Is your focus on optimizing
        developed or maintained?           how a single system processes
                      │                     complex context information?
          ┌───────────┴───────────┐                │
          │                       │        ┌───────┴───────┐
          ▼                       ▼        │               │
         YES                      NO       ▼               ▼
          │                       │       YES              NO
          │                       │        │               │
          ▼                       ▼        ▼               ▼
      Strongly                 Consider   Strongly      Consider a
    Consider A2A              A2A with   Consider MCP   custom solution
                             internal     
                            coordination

Use MCP When:

  • Building a standalone AI application
  • Implementing a RAG (Retrieval Augmented Generation) system
  • Optimizing model inputs is your priority
  • Working with a single service provider
  • Dealing with complex, varied context sources
  • Creating an LLM-based chatbot with memory

Use A2A When:

  • Building a system that coordinates multiple specialized services
  • Integrating with third-party AI capabilities
  • Creating a distributed AI system
  • Implementing complex multi-step workflows
  • Building an ecosystem of interoperable agents
  • Focus is on coordination over context depth

Consider Hybrid Approach When:

  • Building complex systems with both coordination and context needs
  • Creating platforms that will scale to incorporate many services
  • Implementing systems that bridge personal and enterprise use
  • Developing AI orchestration platforms
  • Building systems that need rich context AND service coordination
  • Creating next-generation assistants with multiple capabilities

Expert Recommendation

As both protocols continue to evolve, the most forward-thinking approach is to design systems with protocol flexibility in mind. Consider implementing adapters or abstraction layers that could accommodate either protocol as standards mature.

For enterprise applications starting today, MCP offers immediate benefits with less coordination complexity, while A2A represents the future direction of AI service ecosystems as they become more distributed and specialized.