MCP vs. A2A: Examples, Key Differences, and How to Choose
December 23, 2025
MCP Security, MCP Server, MCP Tools, MCP Use Cases, MCP Use Cases, Model Context Protocol (MCP)
Introducing MCP and A2A
MCP (Model Context Protocol) and A2A (Agent-to-Agent Protocol) are both AI agent protocols that are considered complementary to each other. MCP focuses on an agent’s interaction with tools, while A2A focuses on collaboration between multiple agents. MCP allows an agent to use specific, standardized tools and data sources, while A2A enables agents to communicate with each other to solve more complex, multi-step tasks.
Key aspects of MCP (Model Context Protocol):
Purpose: Standardizes an individual agent’s interaction with external tools, APIs, and data sources.
Function: It defines a clear contract for how an agent should send input and receive output from a tool.
Structure: Operates in a client-server model, where the agent is the client and the tool is the server.
Key aspects of A2A (Agent-to-Agent):
Purpose: Standardizes communication and coordination between autonomous agents across platforms or vendors.
Function: Defines how agents exchange goals, delegate tasks, and synchronize progress to solve complex workflows.
Structure: Operates in a peer-to-peer or decentralized messaging model, enabling agents to initiate, respond to, or coordinate actions without central control.
MCP lets an AI agent call external tools through a structured request/response flow. The agent sends a JSON‑RPC request to an MCP server, which exposes functions such as API calls or data lookups. The server processes the request and returns a result in a predictable format. This setup makes tool usage consistent and avoids custom integrations for each application.
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The protocol uses JSON‑RPC 2.0 over HTTP. It supports streaming via SSE, allowing clients to connect to remote servers that publish capabilities. This structure keeps communication simple while providing a standard way to invoke tools and deliver structured outputs back to the agent.
Example: A client agent sends a request to call a tool named get_weather. The request includes the tool name and arguments, such as the target location. The server receives this structured call, performs the lookup, and returns the results as content blocks. The response indicates whether the call succeeded and includes the text output.
This example shows how MCP enforces clear structure: the client calls a tool through tools/call, passes arguments, and receives a typed content response that the agent can use in its next reasoning step.
A2A defines how autonomous agents communicate using JSON messages over HTTP. Each agent exposes capabilities through metadata, allowing other agents to discover and understand what it can do. Communication centers around task messages that move through states such as submitted, working, and completed. This stateful flow allows agents to coordinate without exposing internal logic.
Agents exchange context using parts (such as text or other content types) and return outputs as artifacts. The protocol supports streaming updates via SSE and webhooks, enabling agents to collaborate on tasks that require intermediate progress or long-running operations.
Example:
Below is a simple code example (in Python) showing how one agent (AgentA) delegates parts of a job to two downstream agents (AgentB and AgentC) via A2A. AgentA receives a user request to plan a trip to Paris, AgentB handles flights, while AgentC handles hotel booking.
import requests
import uuid
import json
# Utility to send a task to a remote agent via A2A
def send_task(agent_endpoint: str, agent_token: str, task_id: str, user_message: str):
headers = {
"Authorization": f"Bearer {agent_token}",
"Content-Type": "application/json"
}
body = {
"jsonrpc": "2.0",
"id": task_id,
"method": "tasks/send",
"params": {
"taskId": task_id,
"messages": [
{
"role": "user",
"parts": [
{
"type": "text",
"text": user_message
}
]
}
]
}
}
resp = requests.post(agent_endpoint, headers=headers, json=body)
resp.raise_for_status()
return resp.json()
# AgentA logic
def agentA_handle_request(user_request):
# 1. Decide how to delegate
# Example: user_request = "Plan a trip to Paris next June for 5 days, 2 adults"
flight_task_id = str(uuid.uuid4())
hotel_task_id = str(uuid.uuid4())
# 2. Call AgentB (FlightAgent)
flight_agent_endpoint = "https://agentB.example.com/a2a"
flight_agent_token = "TOKEN_B" # normally obtained via auth flow
flight_msg = f"Find flights to Paris for {user_request}"
flight_resp = send_task(flight_agent_endpoint, flight_agent_token, flight_task_id, flight_msg)
print("Flight task response:", flight_resp)
# 3. Call AgentC (HotelAgent)
hotel_agent_endpoint = "https://agentC.example.com/a2a"
hotel_agent_token = "TOKEN_C"
hotel_msg = f"Book hotel for 2 adults in Paris for 5 days next June"
hotel_resp = send_task(hotel_agent_endpoint, hotel_agent_token, hotel_task_id, hotel_msg)
print("Hotel task response:", hotel_resp)
# 4. Aggregate results (simplified - in a a real scenario, we
# would poll for completion, handle artifacts etc.)
return {
"flight_task_id": flight_task_id,
"hotel_task_id": hotel_task_id
}
if __name__ == "__main__":
result = agentA_handle_request("Plan a trip to Paris next June for 5 days, 2 adults")
print("Delegated tasks:", result)
MCP vs. A2A: Key Differences
1. Primary Focus
MCP focuses on equipping a single AI agent, such as a large language model, with standardized access to external tools, APIs, and structured data sources. Its core purpose is to help the model extend its capabilities beyond its training data by safely integrating runtime data or tool functionality into its reasoning process. This turns the model into a more capable assistant that can interact with live systems.
A2A is designed for collaborative systems composed of multiple agents. Its primary role is to let agents coordinate with each other, negotiate roles, delegate sub-tasks, and combine results. Instead of enhancing a single model’s access to tools, A2A builds a framework for agents to form distributed workflows and solve problems collectively.
2. Communication Flow
MCP uses a structured client-server interaction model. The agent (client) sends requests to an MCP server that exposes specific capabilities (like reading a file, calling an API, or returning a prompt). This is done through defined messages following a standard lifecycle using JSON-RPC 2.0. The server responds with outputs that the agent can use in its response to the user. The flow is predictable, task-specific, and synchronous.
A2A enables decentralized, peer-to-peer communication among autonomous agents. Each agent publishes an Agent Card, a JSON metadata file describing its skills, inputs, authentication methods, and communication endpoints.
Agents use this metadata to discover and interact with each other. Tasks are created, handed off, and updated asynchronously. Messages can include task parts (inputs) and artifacts (outputs), and agents may exchange context, follow-up questions, or partial results. The communication model supports flexible and ongoing coordination across agents.
3. Scope of Use
MCP is best applied when a single agent must interact with multiple external tools, databases, or APIs during a task. It’s suitable for contexts where the tool usage is well defined and needs to be tightly controlled, such as coding assistants fetching project files, chatbots accessing internal company data, or LLMs querying APIs for fresh content. Its value lies in making tools discoverable and usable at runtime, without hardcoding access.
A2A is aimed at larger systems where multiple agents with distinct roles or specialties need to cooperate. These agents might be created by different teams or vendors and run on different infrastructures. A2A enables a system to break down a high-level task into subtasks and distribute them across multiple agents. It is suited for dynamic, multi-step processes like onboarding new employees, lead qualification in sales, or multi-agent research.
4. Implementation Complexity
MCP is simpler to implement in isolated environments. The protocol uses a fixed set of message types, and the connection model is direct and short-lived. Developers only need to implement standardized interfaces between the model and the tools. It’s particularly accessible for teams who already have APIs or services and want to connect them to LLMs in a secure and structured way.
A2A introduces higher complexity due to its need for discovery, task management, and asynchronous messaging. Developers must support agent metadata publication, dynamic task assignment, lifecycle tracking, and potentially handle fallback or retries. A2A systems also need to be resilient to agent failures, support long-running tasks, and ensure secure peer-to-peer communication between agents.
5. Use Cases
MCP is suitable when an LLM needs structured access to specific resources to complete a task. Examples include:
A coding assistant fetching function definitions or file contents from a developer’s project
A financial agent accessing internal APIs to generate updated forecasts
A data analyst chatbot pulling in real-time database metrics to answer queries
A customer support agent summarizing email threads and querying order histories
A2A shines in multi-agent workflows where responsibilities are divided across roles or domains. Examples include:
A hiring platform with separate agents for sourcing, screening, and interview scheduling
A cross-functional onboarding process involving HR, IT, and compliance agents
A research assistant coordinating with specialized knowledge agents to answer complex queries
A support platform where one agent handles triage while others resolve based on area of expertise
A2A vs. MCP: How to Choose
Choosing between A2A and MCP, or deciding how to use them together, depends on the structure of your AI system and the types of tasks it needs to handle. These protocols solve different problems and are often complementary in complex applications.
Use A2A when you need agent collaboration
A2A is essential when your system requires multiple autonomous agents to work together. If tasks are distributed across specialized agents (e.g., HR, IT, legal), A2A provides the coordination layer. It allows agents to communicate, delegate responsibilities, and track task progress without centralized control. This is especially valuable when agents are independently developed or run on different platforms.
Use MCP when you need tool access
MCP is the right choice when agents need access to structured tools, APIs, or data sources. It standardizes how an agent interacts with external systems, ensuring consistent behavior across tools. If an agent needs to fetch real-time data, perform lookups, or call external services, MCP provides a clean, schema-based way to do so.
Use both when you need modular, scalable systems
In complex workflows like onboarding or enterprise automation, you often need both protocols. A2A handles the coordination between agents, while MCP handles how each agent interacts with tools. For example, an agent may receive a task via A2A and use MCP to access the necessary systems to complete it. This layered architecture makes your system modular, where adding a new tool or agent doesn’t require reworking the rest of the pipeline.
This combined approach allows agents to operate autonomously, while also leveraging shared infrastructure and toolsets. It also supports compliance, human oversight, and progressive scaling of capabilities as your agent ecosystem grows.
