A2A vs MCP: Agent Communication vs Tool Access

Different Problems, Different Protocols

MCP solves the tool integration problem. An AI agent needs to query a database, read a file, call an API, or search the web. MCP provides a standard protocol for discovering these capabilities, invoking them, and handling results. The relationship is vertical: the agent is above, the tool is below, and MCP is the connection between them. The agent is in control, and the tool executes what the agent requests.

A2A solves the agent coordination problem. A complex task might require multiple specialized agents, each with different expertise, different model configurations, or different tool access. An orchestrating agent needs to delegate subtasks to specialist agents, monitor their progress, and combine their results. A2A provides a standard protocol for this inter-agent communication. The relationship is horizontal: both sides are autonomous agents, and A2A is the protocol they use to negotiate, delegate, and collaborate.

The distinction becomes clear with a concrete example. An enterprise customer support system might have a routing agent that receives incoming requests, a billing agent that handles payment questions, and a technical agent that handles product issues. The routing agent uses A2A to delegate a billing question to the billing agent. The billing agent uses MCP to connect to the payment database, the CRM system, and the refund API. A2A handles the agent-to-agent delegation, and MCP handles the agent-to-tool connections.

A2A Architecture

Google introduced A2A in April 2025 as an open protocol for agent interoperability. A2A defines how agents discover each other's capabilities, exchange tasks, negotiate execution parameters, and share results. It uses HTTP-based communication with JSON payloads and supports both synchronous request-response patterns and asynchronous long-running task patterns.

Each A2A agent publishes an Agent Card, a JSON document that describes the agent's capabilities, accepted input formats, output formats, and communication preferences. Other agents read these Agent Cards to understand what tasks each agent can handle and how to interact with it. This discovery mechanism is analogous to MCP's capability negotiation but operates at the agent level rather than the tool level.

A2A defines a Task lifecycle that supports long-running operations. An orchestrating agent can submit a task to a specialist agent, receive a task ID, poll for progress updates, and eventually receive the completed result. This asynchronous pattern is essential for agent tasks that might take minutes or hours to complete, unlike MCP tool calls that typically return results within seconds.

Communication in A2A can include structured data, unstructured text, and even multi-modal content like images. This flexibility reflects the reality that agent-to-agent communication is more varied than agent-to-tool communication. An agent delegating a data analysis task might send a dataset, a set of instructions, and expected output format, requiring a richer communication protocol than MCP's tool call and result pattern.

Where They Overlap

Both protocols address interoperability, but at different levels. MCP provides interoperability between agents and tools: any MCP client can use any MCP server. A2A provides interoperability between agents: any A2A-compatible agent can delegate tasks to any other A2A-compatible agent, regardless of which model powers each agent or which framework each is built with.

Both protocols support capability discovery. MCP servers advertise their tools, resources, and prompts. A2A agents advertise their task capabilities through Agent Cards. In both cases, the discovery mechanism allows the initiating party to understand what the other side can do before making a request.

Both protocols are now governed by the same body. In December 2025, Anthropic donated MCP to the Linux Foundation's Agentic AI Foundation (AAIF), where it joined Google's A2A. The AAIF, co-founded by Anthropic, OpenAI, Block, Google, and Microsoft, provides vendor-neutral governance for both protocols. This co-governance reflects the industry consensus that MCP and A2A are complementary standards that belong together.

Using Both Together

In production multi-agent systems, A2A and MCP serve distinct roles in the architecture. The orchestration layer uses A2A. When a supervisor agent decides that a task requires a specialist, it uses A2A to delegate the task, negotiate parameters, and receive results. The execution layer uses MCP. When a specialist agent needs to query a database, read a file, or call an API to complete its assigned task, it uses MCP to connect to the appropriate tools.

This separation of concerns keeps each protocol focused on what it does best. A2A handles the complex negotiation, progress tracking, and result aggregation needed for agent-to-agent coordination. MCP handles the structured invocation, permission management, and result formatting needed for agent-to-tool integration. Neither protocol needs to handle the other's concerns, which keeps both protocols simpler and more maintainable.

A practical architecture might look like this: a user request arrives at an orchestrator agent that uses A2A to delegate research to a research agent and data analysis to an analytics agent. The research agent uses MCP to connect to web search and document database servers. The analytics agent uses MCP to connect to SQL databases and data processing tools. Both specialist agents complete their tasks, return results to the orchestrator through A2A, and the orchestrator synthesizes the final response for the user.

When You Need Each One

You need MCP when your AI agent needs to access external tools and data sources. This covers the vast majority of AI agent use cases: coding assistants, data analysis tools, customer support bots, and any application where the model needs to interact with systems beyond its training data. If your agent uses tools, you benefit from MCP.

You need A2A when you are building multi-agent systems where autonomous agents need to coordinate. This covers orchestrator patterns (a supervisor delegating to specialists), pipeline patterns (agents processing data sequentially), and marketplace patterns (agents offering services that other agents can consume). If you have a single agent that uses tools, A2A is not needed. If you have multiple agents that need to work together, A2A provides the coordination layer.

Most teams start with MCP for tool integration and add A2A only when they scale to multi-agent architectures. A single well-equipped agent with good MCP server connections can handle many use cases that might seem like multi-agent problems. Multi-agent coordination adds complexity, and A2A is worth adopting when that complexity is justified by genuine specialization needs rather than when it can be avoided by giving a single agent better tools.