A2A, MCP: What Every AI Engineer Needs to Know

What is MCP (Model Context Protocol)?

The Model Context Protocol (MCP), introduced by Anthropic, defines a standardized interface for supplying structured, real-time context to large language models (LLMs).

💡 How it works💡

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Core Functionalities

Contextual Data Injection

MCP lets you pull in external resources — like files, database rows, or API responses — right into the prompt or working memory. All of it comes through a standardized interface, so your LLM can stay lightweight and clean.

Function Routing & Invocation

MCP also lets models call tools dynamically. You can register capabilities like searchCustomerData or generateReport, and the LLM can invoke them on demand. It’s like giving your AI access to a toolbox, but without hardwiring the tools into the model itself.

Prompt Orchestration

Rather than stuffing your prompt with every possible detail, MCP helps assemble just the context that matters. Think modular, on-the-fly prompt construction — smarter context, fewer tokens, better outputs.

Implementation Characteristics

• Operates over HTTP(S) with JSON-based capability descriptors
• Designed to be model-agnostic — any LLM with a compatible runtime can leverage MCP-compliant servers
• Compatible with API gateways and enterprise authentication standards (e.g., OAuth2, mTLS)

Engineering Use Cases

• ➀ LLM integrations for internal APIs: Enable secure, read-only or interactive access to structured business data without exposing raw endpoints.
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• ➁ Enterprise agents: Equip autonomous agents with runtime context from tools like Salesforce, SAP, or internal knowledge bases.
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• ➂ Dynamic prompt construction: Tailor prompts based on user session, system state, or task pipeline logic

What is A2A (Agent-to-Agent Protocol)?

The Agent-to-Agent (A2A) Protocol, introduced by Google, is a cross-platform specification for enabling AI agents to communicate, collaborate, and delegate tasks across heterogeneous systems.

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Unlike ACP’s local-first focus or MCP’s tool integration layer, A2A addresses horizontal interoperability — standardizing how agents from different vendors or runtimes can exchange capabilities and coordinate workflows over the open web.

Protocol Overview

A2A defines a HTTP-based communication model where agents are treated as interoperable services. Each agent exposes an “Agent Card” — a machine-readable JSON descriptor detailing its identity, capabilities, endpoints, and authentication requirements.

Agents use this information to: 1. Discover each other programmatically 2. Negotiate tasks and roles 3. Exchange messages, data, and streaming updates

A2A is transport-layer agnostic in principle, but currently specifies JSON-RPC 2.0 over HTTPS as its core mechanism for interaction.


Core Components


Agent Cards JSON documents describing an agent’s capabilities, endpoints, supported message types, auth methods, and runtime metadata.
A2A Client/Server Interface Each agent may function as a client (task initiator), a server (task executor), or both, enabling dynamic task routing and negotiation.
Message & Artifact Exchange Supports multipart tasks with context, streaming output (via SSE), and persistent artifacts (e.g., files, knowledge chunks).
User Experience NegotiationAgents can adapt message format, content granularity, and visualization to match downstream agent capabilities.


Security Architecture



• OAuth 2.0 and API key-based authorization
• Capability-scoped endpoints — agents only expose functions required for declared interactions
• Agents can operate in “opaque” mode — hiding internal logic while revealing callable services

Implementation Characteristics



• Web-native by design: built on HTTP, JSON-RPC, and standard web security
• Model-agnostic: works with any agent system (LLM or otherwise) that implements the protocol
• Supports task streaming and multi-turn collaboration with lightweight payloads



Protocols Compared Side-by-Side




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Complementary or Competitive?

A2A + MCP

A2A and MCP aren’t fighting each other — they’re solving totally different parts of the agentic AI puzzle, and they actually fit together pretty nicely.


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Think of MCP as the protocol that lets AI agents plug into the world. It gives them access to files, APIs, databases — basically, all the structured context they need to do something useful. Whether it’s pulling real-time sales data or generating a custom report, MCP handles the connection to tools and data.

Now layer on A2A. This is where agents start collaborating. A2A gives them a shared language and set of rules to discover each other, delegate tasks, and negotiate how they’ll work together — even if they’re built by different vendors or running on different platforms.

So here’s a simple way to think about it:


• ⟢ MCP connects AI to tools.
• ⟢ A2A connects AI to other AI.

Together, they form a strong modular base for building smart, collaborative systems.

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In short: we’re early. But how we build and adopt these standards now will shape whether AI agents become a cohesive ecosystem — or a patchwork of silos.