Agentic Revolution: A2A (Agent 2 Agent Protocol) & Model Context Protocol (MCP) — Rivals or Allies in AI Evolution? Comparison

AI Agents are hot cakes that are being sold world over and going through hype phase. Sales teams are working overtime to catch the tide and ride the wave. Product teams are releasing features at a much faster pace to keep up and math the sales pitches. There are two prominent runners that have emerged in solving interoperability aspects of enterprise meeting Agentic revolution. Agent to Agent collaboration and connecting agents to enterprise systems with out building a full blown AI Model on top of your enterprise.

Compare A2A and MCP

	A2A Agent to Agent Protocol	MCP Model Context Protocol
Released By	Google	Anthropic
Focus Area	Standardise how independent often completely opaque Ai agents communicate with each other	Standardise how agents connect and interact with APIs, data sources and many external resources with out building a AI ML Model on top of those systems
Use case	Enable Agents to discover each others capabilities Enable Agents to negotiate interaction modalities like text or files or structured data etc Enable Agents to have long running tasks Enable Agents to share conversation context Enable Agents to get back multi part results	Enable Agents to query external data sources Enable LLM to call external data sources
Autonomy	Built for more autonomous collaboration	Built for specific response from MCP server
Interaction	Involves ongoing tasks, context sharing and negotiations between agents	Involves single request-response cycle from MCP client or Host to MCP server
Modal	Dynamic Multi modal collaboration like text, files, structured data etc	Fixed single modal collaboration
Context	Context sharing is part of the negotiations	There is no context sharing that happens from MCP host or client to the MCP server
Context Sharing	There is context sharing as part of the protocol and is fully stateful	There is no context sharing between MCP Host/Client and MCP server
Structure of Communication	Uses reason, plan, multiple tools so is not fully well defined	Well defined structured inputs and outputs from MCP servers
Behaviour	Emergent behaviour and less predictable	Predictable behaviour and MCP host / client to MCP server is transactional in nature
Complexity	Can be used to achieve more complex goals and address broader tasks	Can be used to achieve specific tasks using MCP server
State	Stateful sharing of context across in A2A	There is no state management needed from MCP Host/Client to the MCP server
Duration	Can be very long running tasks	Is normally short lived tasks from MCP host/client to MCP Server
Message format	JSON-RPC 2.0	JSON-RPC 2.0
Transport Type	Server-Sent Events (SSE) over HTTP(S) webhook	stdio Server-Sent Events (SSE) over HTTP(S)
Discovery	Using well known URI …/.well-known/agent.json	Loaded as a tool for MCP host or as endpoints in MCP clients while coding

AI Puzzle A2A and MCP Role

In the AI puzzle A2A and MCP solve different parts of the puzzle.

A2A Scope

A2A plays to its strength for inter-agent collaboration and solving the agent interoperability problem.

MCP Scope

MCP plays to its strength for wrapping backend systems as a data source for AI interactions with out writing AI models for every data and also brings live data to the AI realm.

A2A and MCP, Rivals or Allies?

A2A and MCP are not mutually exclusive. They are hyper complementary and address different parts of the AI puzzle to bring agentic systems to reality for customers.