MCP & A2A Protocols Explained (With a Very Illegal Analogy)
Hey Geeks, I hope you all, and your family/friends/girlfriends/boyfriends, are doing well. I learnt and cleared a new concept today that's actually pretty old, but as they say, it's never too late. So let's get started with A2A Protocol & MCP. After understanding both concepts, I've linked a hands-on lab by Google For Developers at the end, which you should not miss by any chance (at least give it a read, even if you don't try it hands-on).
TL;DR
- MCP (Model Context Protocol): standardizes how an AI agent talks to tools and data sources.
- A2A (Agent2Agent Protocol): standardizes how one AI agent talks to another AI agent.
- Both together form the backbone of modern multi-agent systems.
- A Google codelab is linked at the end so you can actually build one.
1. MCP - Model Context Protocol
Let's start with an example.
Suppose you're a drug dealer, and you make very profitable deals selling different types of drugs (I can't name them here, but lowkey, you all know what I mean, haha). Every month, sometimes every day, you have to deal with different mafias or suppliers just to keep your inventory stocked. But each mafia sells only one type of product, so you end up juggling multiple suppliers to get everything you need. And the moment one of them changes something, a pickup rule, a price, a contact number, you have to adjust your entire setup around it. It gets messy fast. And it's not just your business; other industries were dealing with the exact same headache.
So, looking at this problem, the Karta Dharta (CEO) of Anthropic came up with an idea and a solution: MCP (Model Context Protocol).
MCP, in your terms as a drug dealer:
Instead of personally negotiating with every mafia, one for weed, one for coke, one for whatever else, each with their own contact number, their own pickup rules, their own "bro don't call after 9 PM" attitude, MCP basically says: let's standardize how everyone talks to each other.
Every supplier (weed mafia, coke mafia, pill mafia) exposes their inventory, pricing, and pickup process in the exact same format. You don't need to remember ten different ways of dealing with ten different mafias anymore. You just speak "MCP," and everyone understands you, because they're all plugged into the same protocol.
Basically:
- Before MCP: 10 different relationships, 10 different rules, 10 different headaches
- After MCP: one common language, one system, everyone plugs in the same way
In real dev terms: MCP standardizes how an AI model (like Claude or Gemini) talks to external tools and data sources: databases, APIs, file systems, whatever. Instead of writing custom integration code for every single tool, the tool simply exposes an MCP-compatible interface, and any MCP-compatible AI can use it right out of the box.
That's MCP, in technical terms, and in your secret mafia terms.
2. A2A - Agent2Agent Protocol
Now here's the plot twist. Your drug business is booming, thanks to MCP, no more supplier headaches, so you've levelled up. You're not just a dealer anymore, you're running an operation. And every operation needs a crew.
Meet Pablo, our dealer-turned-kingpin. Pablo now has a whole team working under him:
- A lawyer agent, who handles it if the cops show up
- A money-laundering agent (let's call him the "accountant" for LinkedIn purposes), who cleans Pablo's cash through five shell businesses
- An enforcer agent, who "resolves disputes" when a customer doesn't pay on time
- And Pablo himself, the one planning the whole operation, deciding who does what, and when
Here's the thing: Pablo doesn't personally do the laundering or the lawyering. He just tells his accountant agent, "clean this ₹50L," and it goes off, does its own thing (talks to banks, shifts numbers around, whatever shady stuff accountants do), and comes back with "done, boss, all clean." Pablo doesn't need to know how it got done. He just needs to know it can be done, and trust that it'll report back.
That right there is A2A. It's not about talking to a tool, that's MCP's job. It's about agents talking to other agents as peers, each with their own skills and their own way of working, coordinating on a task without needing to share the same brain or database.
Client Agent vs. Remote Agent, with the same crew:
- Pablo = the Client Agent. He's the one who decides "I need this task done," picks the right specialist for the job, and sends over the task.
- The lawyer / accountant / enforcer = the Remote Agents. They operate completely independently: different skillset, different "brain," maybe even built on an entirely different framework, but they can still receive Pablo's task, execute it, and hand back a result.
In technical terms:
- The Client Agent is the one that initiates a request. It discovers what a remote agent can do by checking its Agent Card, basically a resume: "here's what I'm good at, here's how to reach me," and then sends it a task.
- The Remote Agent receives that task, works on it independently (using its own tools, models, and memory, none of Pablo's business how), and returns the result back to the client agent in a structured, standardized way, with no custom integration required.
But wait, how does Pablo even find the right guy in the first place?
Here's the underworld version: Pablo doesn't randomly ask around town for "someone who's good at laundering money." That's how you get caught. Instead, every specialist in this world leaves their "resume" at one fixed, predictable spot, a spot everyone in the business already knows to check. No introductions needed, no middleman required. Pablo just walks up to that spot, reads the resume, and knows exactly what the guy does and how to reach him.
In A2A, that fixed spot is a real, actual file: /.well-known/agent.json
Every remote agent hosts this file at a standardized URL path on its domain, something like https://accountant-agent.com/.well-known/agent.json. It's the machine-readable version of the Agent Card, and it typically contains:
- The agent's name and description ("what I do")
- Its capabilities/skills ("what tasks I can handle")
- Its endpoint URL ("where to actually send me work")
- Its authentication requirements ("how you prove you're allowed to talk to me")
Because it always lives at this exact, predictable path, similar in spirit to how robots.txt always sits at a website's root, any client agent can discover it automatically. No manual setup, no hardcoded config, no "let me DM you my API details." Pablo's client agent just requests that URL, reads the JSON, and instantly knows whether this remote agent is the right one for the job.
That's the whole magic of A2A: agents built on completely different frameworks (LangGraph, CrewAI, or whatever else) can still discover and collaborate with each other, because they're all speaking the same protocol and checking the same well-known spot.
3. MCP vs. A2A - In One Breath
- MCP = you (the agent) talking to your stuff: tools, databases, APIs. Agent to Tool.
- A2A = you (the agent) talking to your guys: other agents who can do things you can't. Agent to Agent.
Or in mafia terms: MCP is how you talk to your suppliers. A2A is how your crew members talk to each other while you're busy planning the next move.
Use MCP when your AI needs to fetch or do something: pull data, call an API, hit a database.
Use A2A when your AI needs to delegate to someone else who's better suited for the job: another specialized agent.
Most real-world systems in 2026 use both, together.
4. Practical Applications
Here's where this actually gets useful for you as a developer:
- Customer support systems: an MCP layer pulls order and ticket data from your CRM and helpdesk tools, while an A2A layer routes complex cases between a billing agent, a refunds agent, and a technical-support agent, each specialized and independently maintained.
- Research and analysis assistants: MCP connects the agent to search tools, internal documents, and databases, while A2A lets it delegate a sub-task, like summarizing a report or running a calculation, to a separate specialist agent instead of doing everything itself.
- Enterprise workflow automation: a procurement agent (client) negotiates with vendor agents (remote) exposed by supplier companies, each pulling live pricing and inventory data through their own MCP connections.
- Personal AI assistants: a "planner" agent coordinates a calendar agent, a travel-booking agent, and an expense-tracking agent, each handling its own job and reporting back, instead of one giant agent trying to do everything at once.
This combination, MCP for tool access, A2A for agent collaboration, is essentially the backbone of how multi-agent systems are being built today.
5. Go Build Something
If this made sense (and hopefully made you laugh a little), don't stop here. Google For Developers has a genuinely solid, hands-on codelab where you actually build a multi-agent system using these exact concepts:
🔗 Building a Multi-Agent System - Google Codelabs
Even if you don't run the code yourself, skim through it, seeing the real architecture will make everything above click even harder.
That's it from me for today, Geeks. If this helped you understand MCP and A2A without putting you to sleep, smash that like, drop a comment, and follow for more such fun-but-actually-useful content.
And if you want me to go full nerd next time, actual architecture diagrams, real code, the whole deep-dive treatment, comment "DEEP DIVE" below, and I'll cook up part 2.
Until then, stay curious (and please don't actually become a drug dealer, this was just for the plot). ✌️
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