MCP vs Multi-Agent vs A2A
What is the difference between Model Context Protocol (MCP) and Google’s Agent-to-Agent (A2A)? Where does multi-agent AI fit in?
These terms are often used interchangeably, but they describe different layers of capability.
Let’s break it down using a task that LLMs excel at: generating a patient summary from all available data.
Scenario: Generating a Patient Summary
Imagine asking an AI system to summarize a patient’s complete record, including EHR data, lab results, imaging reports, and clinical notes.
How this happens depends on the architectural approach.
1. A Single Agent Using MCP
In a single-agent setup, you ask the agent to generate the summary. Using MCP, the agent invokes available tools to retrieve relevant data sources such as:
EHR records
Lab results
Imaging reports
Medication lists
The LLM analyzes the retrieved data and produces a consolidated summary.
In this case, MCP acts as the structured bridge between the model and its tools. It enables the agent to decide which tool to call, pass the correct arguments, and retrieve structured outputs in real time.
The architecture looks like this:
User → Agent → MCP Tools → Data → Agent → Summary
The intelligence remains centralized within a single LLM. MCP simply expands what the LLM can access and execute.
2. The Multi-Agent Approach
Now let’s take this further.
Instead of one agent doing everything, we introduce multiple specialized agents. The initial orchestrator agent delegates specific subtasks to other agents. Each of those agents may have its own LLM and domain specialization.
For example:
An Imaging Analysis Agent interprets radiology reports.
A Clinical Note Extraction Agent identifies key findings from physician notes.
A Medication Reconciliation Agent analyzes active medications and potential interactions.
A final Summarization Agent aggregates outputs from all specialized agents into a cohesive patient summary.
In this architecture, the orchestrator agent effectively uses other agents as tools.
Why introduce this added complexity?
1. Specialization
Different data types require different expertise. A model optimized for radiology interpretation may not be ideal for medication analysis. Multi-agent systems allow each agent to be configured with specialized prompts, fine-tuning, or even different models tailored to specific tasks.
2. Cost Efficiency
Not every task requires a high-end model. A lightweight agent can handle simple retrieval or literature searches, while a more powerful and costly model performs deeper synthesis and reasoning. This layered allocation improves cost-performance tradeoffs.
3. Better Context Engineering
Each agent can have its own system prompt, memory scope, and constraints. This allows fine-grained control over reasoning patterns and reduces context overload within a single monolithic model. In short, multi-agent architecture is a design choice within a system. It is about how intelligence is organized internally.
3. Where A2A Comes In
Agent-to-Agent (A2A) operates at a different level.
While MCP defines how a model interacts with tools, A2A defines how independent agents communicate with each other, potentially across organizational boundaries.
A2A introduces several important capabilities:
Cross-Organizational Communication
A2A is designed for seamless interaction between agents built and hosted by different entities. For example, a hospital’s clinical agent could interact with a research institution’s genomic analysis agent.
Standardized Discovery and Communication
A2A specifies how agents discover each other using structures like Agent Cards. It defines how tasks are described, how responses are structured, and how context is exchanged.
Bidirectional Dialogue
A2A emphasizes ongoing, back-and-forth interaction between agents. Instead of a single function call, agents can clarify requests, request additional context, and negotiate outcomes.
This is fundamentally different from simply exposing one agent as an MCP tool.
If you expose an agent as an MCP tool, it behaves like a callable function. It receives input and returns output.
With A2A, agents communicate more like peers. They engage in structured, potentially natural-language dialogue to complete complex tasks.
Consider prior authorization workflows. A hospital agent might coordinate with a payer agent to exchange documentation, clarify missing information, and negotiate approval steps. This requires iterative interaction, not just a single API call.
Putting It All Together
Here is a simple way to think about the differences:
MCP empowers a single agent to access data and tools in real time.
A multi-agent architecture organizes specialized intelligence within a system.
A2A enables communication between independent agents, potentially across organizations.
MCP enhances capability. Multi-agent design enhances specialization and orchestration. A2A enhances collaboration at scale.
The future of intelligent healthcare systems will likely combine all three. Agents will use MCP to access tools, operate within multi-agent architectures for specialization, and communicate via A2A when collaborating across systems.
The next wave of healthcare AI will not be defined by isolated models, but by systems of agents that can reason, coordinate, and act together.