What are the differences and relationship between MCP and A2A protocol?
After MCP (Model Context Protocol) from Anthropic getting popularity, recently Google has introduced another protocol - A2A (Agent to Agent Protocol). A natural question arises is how are they related to each other? Are they complementing or competing with each other?
In this post, let’s try to demystify the two protocols.
Current AI Agentic System (or Multi-agent System) development has two challenges:
- Integrating agent with tools and data
- Agent to agent communications
In a nutshell, MCP (Model Context Protocol) tackles the first challenge, and A2A (Agent to Agent Protocol) aims to tackle the second one.
MCP
MCP (Model Context Protocol) from Anthropic is an open protocol that standardizes how applications provide context to LLMs, like USB-C port for AI applications.
Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools. - MCP documentation
According to HuggingFace, an agent has an LLM as its brain and capabilities and tools as its body.
- Tools available to an agent allow it to access new knowledge beyond the knowledge cut-off date, such as retrieving social media data or the current weather.
- Capabilities represent everything the agent is equipped to do. One popular capability example is using a memory component to remember things, such as previous conversations and external data.
Look into the example provided by Anthropic, an agent (MCP host) can access additional context from local data sources or remote services via MCP protocol.
- MCP Hosts: Programs like Claude Desktop, IDEs, or AI tools that want to access data through MCP
- MCP Clients: Protocol clients that maintain 1:1 connections with servers
- MCP Servers: Lightweight programs that each expose specific capabilities through the standardized Model Context Protocol
- Local Data Sources: Your computer’s files, databases, and services that MCP servers can securely access
- Remote Services: External systems available over the internet (e.g., through APIs) that MCP servers can connect to
The MCP Server is a foundational component which provides tools, resources, and capabilities to clients via APIs and endpoints. More specifically:
- Exposing tools that clients can discover and execute
- Managing resources with URI-based access patterns
- Providing prompt templates and handling prompt requests
- Supporting capability negotiation with clients
- Implementing server-side protocol operations
- Managing concurrent client connections
- Providing structured logging and notifications
A2A
A2A (Agent to Agent Protocol) from Google is an open protocol that provides a standard way for agents to collaborate with each other, regardless of the underlying framework or vendor.
A2A facilitates communication between a "client" agent and a “remote” agent.
A client agent is responsible for formulating and communicating tasks, while the remote agent is responsible for acting on those tasks in an attempt to provide the correct information or take the correct action.
This interaction involves several key capabilities:
- Capability discovery: Agents can advertise their capabilities using an “Agent Card” in JSON format, allowing the client agent to identify the best agent that can perform a task and leverage A2A to communicate with the remote agent.
- Task management: The communication between a client and remote agent is oriented towards task completion, in which agents work to fulfill end-user requests. This “task” object is defined by the protocol and has a lifecycle. It can be completed immediately or, for long-running tasks, each of the agents can communicate to stay in sync with each other on the latest status of completing a task. The output of a task is known as an “artifact.”
- Collaboration: Agents can send each other messages to communicate context, replies, artifacts, or user instructions.
- User experience negotiation: Each message includes “parts,” which is a fully formed piece of content, like a generated image. Each part has a specified content type, allowing client and remote agents to negotiate the correct format needed and explicitly include negotiations of the user’s UI capabilities–e.g., iframes, video, web forms, and more.
Google highlighted A2A is a complementary to MCP rather than an alternative.
Model Context Protocol (MCP) is the emerging standard for connecting LLMs with data, resources, and tools. We already observe MCP standardizing ‘function calling’ across different models and frameworks. This is creating an ecosystem of tool service providers and dramatically lowering the complexity to connect agents with tools and data. We expect this trend to continue as more frameworks, service providers, and platforms adopt MCP.
A2A is focused on a different problem. A2A is an application level protocol that enables agents to collaborate in their natural modalities. It allows agents to communicate as agents (or as users) instead of as tools. We hope that A2A gains adoption as a complement to MCP that enables ecosystems of agents and will be working in the open with the community to make this happen.
Simply put, MCP for tools and A2A for agents.
Google even recommends that applications model A2A agents as MCP resources (represented by their AgentCard). The frameworks can then use A2A to communicate with their user, the remote agents, and other agents as illustrated below.
Discussion
With a peaceful coexisting scenario of the two standards, each agent (MCP host) can have their own connected MCP servers for accessing tools and resources via the MCP protocol. Additionally, these agents communicate with each other through the A2A protocol, as discussed in SwirlAI’s post. This applies when we treat agents as agents as independent entities rather than tools. However, this might change when agents are treated as special tools that can also be exposed via MCP servers.
It will be interesting to see how developers decided choose to adopt these standards - wheter one or both - as their decisions will eventually shape the future of these protocols. Also, MCP and A2A are both new and will keep evolving, with the possibility to extend functionalities to be better positioned in Agentic AI ecosystem.
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