As agentic systems move from research demos into production, engineers face a practical question: how should autonomous pieces of your system communicate, using a model-centric context protocol (MCP) that exposes tools/data to models, or an agent-to-agent (A2A) protocol that lets agents talk to each other?
Both are “standards” in the emerging stack, but they solve different problems and impose different responsibilities on developers.
MCP (Model Context Protocol) is an open standard introduced to let LLM-based applications connect securely and consistently to external data sources and tools. In short: MCP treats the model as the primary actor and standardizes how a model requests context, tools, and actions from external servers (the “MCP servers”). This reduces bespoke integrations for each model/data pair and addresses the N×M integration problem.
A2A (Agent2Agent) is an explicitly multi-agent communication protocol: it’s about agent discovery, capability negotiation, task handoff, and inter-agent messaging. A2A is designed for scenarios where multiple autonomous agents (potentially from different vendors or frameworks) need to coordinate or delegate subtasks to one another. Think “who is responsible for planning vs. execution vs. fact-checking” in a distributed workflow. Google’s A2A materials emphasize discovery, capability advertisement, and lightweight negotiation primitives as first-class features.
Architecturally, MCP is client–server: the LLM (or client wrapper) queries MCP servers to fetch context or invoke app-level operations; servers return structured context and optionally perform actions. The pattern is ideal when you want a model to remain the central decision-maker but need standardized access to many external services.
A2A, by contrast, treats agents as peers. Each agent advertises what it can do, communicates intents and results, and participates in multi-party coordination. State is often distributed or replicated among participating agents (or managed by a coordinating orchestrator), so you trade the simplicity of a single “model-as-controller” for richer collaboration semantics.
From a developer perspective the differences show up in message semantics:
Because MCP is about standardizing tool access, its schemas emphasize authenticated resource access, context filtering, and provenance. A2A requires richer metadata (capabilities, SLAs, agent identity) and often needs message routing and optional multi-party consensus semantics. The AWS guidance on agent protocols highlights these tradeoffs and lists MCP as a flexible foundation that can be extended for inter-agent use cases, while A2A focuses specifically on inter-agent choreography.
Security models differ substantially. MCP servers are gates to your data: If an LLM can call an MCP endpoint, misconfiguration or insufficient auth can leak private context, where prompt injection and unintended disclosures are practical risks. Production MCP deployments therefore need robust authentication, fine-grained authorization, audit logging, and filtering at the server boundary. Journalistic coverage and industry writeups warn developers to treat MCP endpoints as sensitive, production-grade services.
A2A introduces non-human identities and cross-agent trust problems: agents need to authenticate, verify capabilities, and often require ephemeral scoped credentials for delegated tasks. On the upside, a well-built A2A layer lets you isolate privilege per agent (agent A can only call agent B for X), which helps with the principle of least privilege in complex systems, but it increases protocol complexity and operational surface area.
Use MCP when:
Use A2A when:
In practice, systems often use both MCP and A2A.
MCP is used as the standardized “tool access” layer and A2A for multi-agent orchestration on top. An agent may use A2A is used to find a specialized agent, then that specialized agent may call MCP servers to fetch secure data or perform actions. This layered approach keeps tool access secure and versioned while preserving agent interoperability for coordination.
If you’re building LLM-driven workflows that need reliable, auditable access to data, exposing those resources via a production-grade MCP server simplifies model integrations and reduces custom code. Choosing a partner who already has a turnkey MCP server is even better.
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