DEV Community: Harsh Mistry

MCP & A2A Protocols Explained (With a Very Illegal Analogy)

Harsh Mistry — Tue, 04 Aug 2026 10:07:58 +0000

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). ✌️

Building the AI Manager Every Engineering Team Actually Needs

Harsh Mistry — Fri, 17 Jul 2026 04:48:16 +0000

Use Case Proposal: An AI Manager for Engineering Teams

A Requirements Intelligence Layer We Could Build for Engineering Teams

Logieagle Pvt. Ltd.

Summary

This document proposes an AI Requirements Manager a grounded, citation-backed AI system for engineering teams that could be developed at Logieagle Pvt. Ltd. Unlike general-purpose coding assistants, the system would answer project questions strictly from indexed project sources (specs, tickets, code, and decisions), refuse to guess when context is insufficient, and instead escalate to the project owner. Every clarification would be written back into the knowledge base, so the system gets more accurate the longer a team uses it. This would form Phase 1 of a broader AI Manager concept, which could later extend into learning individual developer patterns for smarter task assignment and estimation.

The gap in today's AI developer stack

Walk into any engineering team in 2026 and you'll find a stack of AI tools bolted onto the workflow: a coding assistant for autocomplete, a chatbot for architecture debates, maybe a search tool to dig through old tickets, and a review bot that comments on pull requests. Each of these tools is good at one narrow thing. None of them is accountable for the thing that actually slows teams down the most not knowing what's actually true about the project.

Ask five developers on the same team "how is this feature supposed to behave in edge case X," and you'll often get five different answers, because the real answer lives scattered across a Slack thread from three months ago, a half-updated spec doc, and someone's memory. AI tools today are happy to paper over that gap. Ask a general-purpose assistant a question it doesn't have grounded context for, and it will still answer fluently, confidently, and sometimes wrong. That's not a productivity gain. That's technical debt with better grammar.

The opportunity here isn't another code-completion tool. It's a layer that sits above the code a system that knows what's true about a project, admits when it doesn't, and gets smarter every time a human corrects it. We refer to the first version of this system as the Requirements Manager, framed here as Phase 1 of a larger AI Manager use case.

What "AI Manager" would actually mean and what it wouldn't

It's worth being precise here, because "AI project manager" gets thrown around loosely. This would not be a bot that assigns tasks based on vibes, or a surveillance layer that scores developers on keystrokes. Phase 1 of this use case would do exactly one job extremely well: give every developer on a project an honest, cited answer to "what's actually correct here and when it doesn't know, say so and go find out.

That last part is the whole point. Most AI tools are optimized to always produce an answer. This use case optimizes for a harder, less flattering metric: knowing the boundary of its own knowledge, and refusing to cross it silently.

How the Requirements Manager would work

1. A single source of grounded truth

The system would ingest everything that currently defines "what's correct" for a project specs, tickets, architecture docs, prior decisions, and the codebase itself into a retrieval layer. When a developer asks a question, the system would not generate an answer from general knowledge; it would retrieve the specific passages, tickets, or code sections that are relevant, and build its answer strictly from those, with a citation back to the source every time.

This alone would solve a problem every team already has: onboarding a new developer, or even a senior one jumping into an unfamiliar module, currently means hunting through five different tools and asking around. Here, the answer would arrive instantly, with a receipt attached.

2. Confidence-gated answers the part that would make it trustworthy

This is the core engineering discipline that would separate a manager from a chatbot. Every retrieval would be scored for how well it actually covers the question being asked:

● High confidence: the system answers directly, with citations.
● Partial confidence: the system answers what it can, and explicitly flags the part of the question it isn't sure about.
● Low confidence: the system does not guess. It stops, and instead raises a specific, well-formed question to the project owner.

That third behavior would be deliberately the hardest one to build, and the most valuable. It requires resisting the natural pull of a language model to fill gaps with something plausible-sounding. This would be treated as a hard rule, not a soft preference because a single wrong "confident-sounding" answer about business logic can cost a team more time than the question would have taken to ask a human directly.

3. A knowledge base that would actually learn

Here's where the loop would close. When a project owner answers one of these clarifying questions, that answer would not just vanish into a chat log it would get written back into the knowledge base as a permanent, versioned source. The next developer who runs into the same ambiguity would get the correct answer instantly, with no clarification needed.

Over time, this would mean the system's "knowledge" isn't static documentation that slowly rots it becomes a living record that only ever gets more accurate, because every correction becomes institutional memory instead of a one-off Slack reply that gets lost in scrollback.

4. Standards, code, and documentation review for free

Because the same knowledge base would hold the team's standards, conventions, and architectural decisions, code and documentation review could become a natural extension rather than a separate product. Reviews would run in two layers: fast, deterministic checks (style, structure, doc completeness) and a semantic pass that checks whether a change actually respects the architecture and decisions recorded in the knowledge base. For this semantic layer, Jules by Google could be used the prompt it works from would not be generic; it would be constructed from the existing work and the codebase itself, so the review is grounded in how the project actually does things rather than a one-size-fits-all standard. And critically, the same rule would apply here as everywhere else in the system: if it can't confidently evaluate something, it marks that item as unresolved rather than guessing that it's fine. An unreviewed edge case flagged honestly is worth far more than a false green checkmark.

System architecture at a glance

Figure 1. How the Requirements Manager would route a developer question from indexed sources through a confidence check to either a cited answer or an owner escalation that updates the knowledge base.

Why this order would make sense

It would be tempting to jump straight to the flashier version of this an AI that learns every developer's individual patterns and assigns tasks automatically. Starting there would not be advisable. Task assignment and time estimation need historical data to be any good, and a system that starts guessing at estimates or assignments without that data is exactly the kind of tool that erodes trust in week one.

Instead, Phase 1 would be scoped to be immediately useful with zero historical data: a project's existing documents and codebase would be enough to start answering questions accurately from day one. It would also be the piece that builds the foundation everything else depends on task assignment or timeline estimation cannot be built intelligently on top of a knowledge base that isn't already trustworthy.

What this would mean for the teams we work with

For a mid-sized dev team, this use case would look like:

● New developers ramping up in days instead of weeks, because tribal knowledge is searchable and cited instead of scattered.
● Fewer repeated questions clogging up senior developers' time the system handles the ones it can answer confidently, and routes only genuinely ambiguous decisions to the people who should be making them.
● A documentation and requirements process that actually improves itself, instead of decaying the moment the person who wrote the spec moves to a different project.
● Code and doc reviews that catch architectural drift early, without adding another tool developers have to context-switch into.

What could come next

Phase 2 of this use case would move into understanding how individual developers work task completion patterns, natural task-to-developer fit, and smarter assignment built on top of the trustworthy foundation Phase 1 would establish.

For now, the thesis is simple: the most valuable thing an AI can do for an engineering team isn't writing more code faster. It's making sure everyone is working from the same, correct, constantly-updated understanding of what "correct" even means and having the honesty to ask when it doesn't know.

That's the kind of use case Logieagle Pvt. Ltd. could build toward.

Frequently asked questions

What is an AI Requirements Manager?

An AI Requirements Manager would be a grounded AI system that answers engineering questions strictly from a project's own indexed sources specs, tickets, docs, and code citing where each answer comes from, and escalating to a human instead of guessing when the available context is insufficient.

How would this differ from a coding assistant like Copilot or Cursor?

Coding assistants focus on writing and completing code inside a file or repository. This use case focuses one level up on whether the team's shared understanding of requirements, decisions, and standards is correct, current, and consistently applied, and it would explicitly refuse to answer when it isn't confident, rather than generating a plausible-sounding guess.

What would happen when the AI doesn't know an answer?

It would not guess. Below a confidence threshold, the system would stop and raise a specific clarifying question to the project owner. Once answered, that clarification would be written back into the knowledge base permanently, so the same ambiguity never has to be resolved twice.

What would come after Phase 1?

Phase 2 would extend the same trustworthy knowledge base into learning how individual developers actually work task completion patterns and natural task-to-developer fit to support smarter, data-backed task assignment.

Logieagle Pvt. Ltd. is a Gandhinagar-based technology team working across AI-driven automation, backend engineering, and enterprise systems integration. Learn more at www.logieagle.com.