Agent or Tool (A2A or MCP)

In the ecosystem of AI communication, there are protocols designed to facilitate interactions between agents, models, and tools. A2A is positioned to complement MCP, addressing a distinct yet related aspect of agent interaction.

The Model Context Protocol (MCP) is an emerging standard focused on connecting Large Language Models (LLMs) with data and external resources.

Purpose: Reducing the complexity involved in connecting agents with tools and data.

Characteristics: Tools are typically stateless and perform specific, predefined functions (e.g., a calculator, a database query).

The Agent2Agent (A2A) protocol is designed to standardize communication between AI agents. It excels in allowing agents deployed in external systems.

Purpose: Enabling agents to collaborate within their native modalities, allowing them to communicate as agents (or as users) rather than being constrained to tool-like interactions.

Characteristics: This enables complex, multi-turn interactions where agents reason, plan, and delegate tasks to other agents. This facilitates multi-turn interactions, such as those involving negotiation or clarification when placing an order.

“Why can’t agents just be tools?”

Google Developer Forums. 2025. “Agents Are Not Tools - Apigee / Knowledge Hub.” June 26. https://discuss.google.dev/t/agents-are-not-tools/192812.