A2A Protocol vs MCP

A2A protocol vs MCP compares two open protocols that connect different parts of an AI system. The Agent2Agent protocol (A2A), introduced by Google in 2025 , standardises communication between independent agents, while the Model Context Protocol (MCP) standardises how one AI application connects to tools, data sources and prompt templates.

• A2A: A protocol through which one agent discovers another agent, delegates a task and receives the result.
• MCP: A protocol through which an AI application discovers and calls tools exposed by servers, using JSON-RPC 2.0 messages.
• Counterpart: In A2A the other side is an autonomous agent with its own reasoning; in MCP it is a tool server that executes deterministic operations.
• Complementary roles: A2A handles agent-to-agent collaboration, whereas MCP handles agent-to-tool integration, so one system can use both.
• Shared goal: Both replace custom point-to-point integrations with open, documented message formats.

For example, an incident triage agent reads the issue tracker and the logs service through MCP, then uses A2A to ask a separate database agent, which queries PostgreSQL through its own MCP server, to investigate a slow query.

Quick Answer

The A2A vs MCP choice depends on what sits on the other side of the connection. Use MCP when an agent needs to call a tool or read data, such as searching issues or running a SQL query. Use A2A when an agent needs to hand a task to another agent that plans its own steps, possibly built by another team or vendor. Most multi-agent systems that span teams need both protocols.

A2A Protocol vs MCP: Comparison Table

When to Use A2A

• Cross-team agents: Separate teams own specialised agents and expose them to each other without sharing internal prompts or tools.
• Long-running delegation: The requested work involves several steps, intermediate reasoning and progress reporting.
• Vendor independence: Agents are built with different frameworks or by different companies and still need to collaborate.
• Opaque expertise: The calling agent needs the result, not access to the specialist agent's data sources.

When to Use MCP

• Direct tool access: The agent itself must search issues, query a database or read logs.
• Deterministic operations: Each action has a clear input schema and a predictable result.
• Reusable integrations: One server per system should serve every compatible host, as described in MCP architecture.
• Fine-grained control: The host must inspect every tool call and result, for example to enforce human-in-the-loop approval.
• Single-agent systems: Only one agent exists, so there is no other agent to communicate with.

Example: Using A2A and MCP Together in an Incident

The engineering team runs a triage agent and a database agent, each owned by a different team, during a checkout incident.

1. MCP tool call: The triage agent calls recent_errors on the logs server and search_issues on the issue tracker server, both through MCP.
2. A2A delegation: Suspecting the database, the triage agent sends a task to the database agent through A2A: investigate slow queries on the orders table.
3. Independent work: The database agent plans its own steps and calls run_query on its PostgreSQL MCP server, which only it can access.
4. A2A result: The database agent returns a summary, such as a missing index, without exposing its credentials or intermediate queries.

The MCP request in step 3 is an ordinary JSON-RPC 2.0 tool call, identical in structure to calls from any other host.

{
  "jsonrpc": "2.0",
  "id": 12,
  "method": "tools/call",
  "params": {"name": "run_query", "arguments": {"sql": "SELECT query, mean_exec_time FROM pg_stat_statements ORDER BY mean_exec_time DESC LIMIT 5"}}
}

• Handled with MCP only: A single agent would need direct access to all three systems, including database credentials.
• Handled with A2A and MCP: Each agent keeps its own tools and permissions, and A2A carries only the task and the result.
• Security boundary: Delegation through A2A limits which agent can touch production data, a common pattern in multi-agent systems.

The difference between MCP and ordinary integrations is covered in MCP vs API vs function calling, and the protocol basics in what is MCP. Frameworks that coordinate several agents are compared in LangGraph vs CrewAI vs AutoGen.