So when AI agents need to connect to the outside
world, maybe they're booking a flight for you
or checking warehouse stock, they have to speak
some language to do that. And right now there
are really two main contenders. The core conflict
is this. Do you optimize for just raw binary
speed or for a more intelligent sort of human
-like understanding? Yeah, that choice kind of
defines everything, doesn't it? It really does.
Welcome back to the Deep Dive, everyone. And
our sources today really zero in on that exact
tension. We're looking at Anthropic's newer AI
-focused model context protocol, or MCP, and
pitting it against Google's, you know, tried
and true workhorse, gRPC. That's Google Remote
Procedure Call. Okay, so here's the plan. First,
we need to establish why these LLMs, these large
language models, even need external connections.
Why can't they just... know everything. Yeah.
What's the fundamental limit? And then we'll
really get into the weeds comparing them. MCPs,
semantic smarts versus GRPCs, production ready
speed. And then importantly, we'll look ahead.
How might these two actually work together? Because
spoiler, it's probably not going to be just one
winner takes all. We think there's a hybrid future
here. Right. Hybrid architecture. That sounds
like where we need to land. OK, let's start with
that necessity. LLMs are. Well, they're amazing
at pattern matching, understanding language.
Incredibly. But they're not all knowing oracles.
They have some really fundamental limits that
mean they have to reach out. Absolutely. The
first big one is what's called the context window
bottleneck. Even these huge models, you hear
about 200 ,000 token context windows, right?
Sounds massive. It does sound massive. But it's
still finite. You just cannot possibly cram,
say, an entire company's customer database, terabytes
of info or all its historical cone or like a
real time financial data feed into that window.
It just doesn't work. Computationally, it's kind
of nuts. And the cost would be astronomical.
It really is like trying to fit the entire Library
of Congress into like a single notebook. Exactly.
It doesn't scale that way. And the second big
problem is knowledge cutoff. The LLM's knowledge
is fundamentally a snapshot in time. Right. Based
on when it was trained. Precisely. Yeah. So it
doesn't matter if the training data was updated
last week. It can't know live real time information
like the weather right now in Phoenix or your
company's specific internal sales numbers for
this quarter or, you know, the gate number for
a flight right now unless it goes and asks. So
the answer isn't just building bigger and bigger
models indefinitely. Nope. The answer is making
the AI agent smarter about how it gets information,
turning it into an orchestrator. Yeah, like a
real -time decision maker. It needs to know how
to query the CRM or the weather API or the flight
status system exactly when needed instead of
trying to hold it all internally. It shifts from
trying to know everything to knowing how to find
everything. Right. A subtle but really crucial
difference. So thinking about that orchestration,
how does it directly help with that snapshot
in time problem you mentioned? Well, the agents
can then fetch real -time, often proprietary
data that simply wasn't part of its original
training. Okay, so that need for external access
brings us neatly to MCP, the Model Context Protocol.
Anthropic built this, what, late? 2024? Yeah,
fairly recent. And they really came at it with
an AI -first philosophy. It's designed to speak
the LLM's language kind of natively. And how
does it do that? What's the underlying tech?
So it's built on JSON RPC 2 .0, which, you know,
for anyone not deep in APIs, is basically a standard
way for programs to call functions on other programs
using simple text, JSON. Which is human -readable
and importantly, very LLM -friendly. They understand
text structures like JSON really well. Exactly.
And MCP structures everything around three core
concepts or primitives. And the key thing is
they all include natural language descriptions.
Okay, what are they? First up, you have tools.
Think of these as specific functions the agent
can use, like get weather or update customer
record. But crucially, they come with descriptions
in plain English saying what they do and when
to use them. Got it. Like instructions included.
What else? Then there are resources. These represent...
Bigger chunks of data or systems the agent might
need to interact with. Maybe like the schema
definition for a whole database. Again, described
naturally. Okay. Tools, resources. And finally,
prompts. These are like templates for interaction.
They help guide the AI on how it should behave
when performing certain tasks, setting contacts,
setting guardrails. Interesting, but you said
the real game changer is something else. Yeah,
the runtime discovery. This is super important.
An agent connects to an MCP server and can just
ask, literally using a command like tools list,
hey, what can you do? And the server responds,
how? It sends back not just function names, but
those full human readable descriptions we talked
about. Use this tool if the user asks about flight
availability. Or use this one for checking inventory
levels. It's like built -in self -documentation
that the AI understands instantly. So the agent
can adapt on the fly if you add a new tool to
the server. Exactly. The agent discovers it immediately.
No need to retrain the whole model or write complex
new integration code just to make the AI aware
of a new capability. It speeds things up massively.
Okay, thinking about that built -in understanding.
What's the really core advantage of having those
natural language descriptions right there in
the protocol? It lets the AI agent figure out
when and why it should use a specific tool all
on its own. Okay, now let's completely shift
perspective. Let's talk gRPC, Google Remote Procedure
Call. This isn't new and AI -focused like MCP.
This is the heavyweight champion from the world
of microservices. Right, been around for, what,
over a decade? At least. And it was built for
one primary thing, speed. raw industrial strength
efficiency. And how did it achieve that speed?
What are its core components? Well, the absolute
foundation is protocol buffers or protobuffs.
Instead of sending text like MCP does with JSON,
protobuffs serialize the data into a super compact
binary format. Think of it like a highly efficient
machine code for data structures. Very small,
very fast to process. Kind of like Lego blocks
of data, really tightly packed. That's a great
analogy. Yeah. Tiny, efficient blocks. Plus,
gRPC is... built on HTTP 2 which allows things
like bi -directional streaming which means means
the client and server can send messages back
and forth simultaneously over the same connection
really important for real -time applications
data streams that kind of thing okay binary speed
efficient streaming and you mentioned battle
tested oh yeah proven scale Google uses gRPC
for tons of its internal systems. We're talking
systems handling billions, maybe trillions of
requests. It's designed for massive production
environments, rock solid. But there's always
a but, isn't there? Yeah. What's the downside
when we think about AI agents? The downside is
what we can call the AI translation gap. GRPC
tells you how to call a service structurally.
The data formats, the function names, it's all
very precise. Okay. But it has zero built -in
semantic context. It doesn't naturally tell an
AI why you'd call this service or when in conversation
it makes sense to use it. That understanding
isn't part of GRPC itself. Oh, I see. So that
creates a need for what? An extra step. Exactly.
You need this middle layer, this AI translation
layer. It's basically custom code that sits between
the AI agent's sort of fuzzy natural language
intention and the very strict technical GRPC
call needed to execute the action. You have to
build that bridge yourself. Two sec silence.
Yeah. You know, honestly, I still wrestle with
prompt drift myself sometimes. Trying to get
an agent to follow a very specific sequence or
technical instruction perfectly, it's hard. So
I definitely get the challenge of bridging that
gap between, you know, what the user means and
the precise technical steps required. It's a
real challenge. It's the cost you pay for leveraging
that high performance but perhaps less flexible
foundation. So if gRPC is so fast, why is that
translation layer, that extra bit of work, really
necessary? Why bother if it adds complexity?
Yeah, that crucial layer is what translates the
human -like goal into the very precise structured
instructions the gRPC system needs to actually
do the job. Okay, that comparison sets us up
perfectly. Let's actually visualize this. Looking
at the architectural flow, how a request actually
moves through the system, really highlights the
core differences. Yeah, let's map it out. With
the MCP flow, it looks simpler on paper. You've
got the LLM agent. It talks to an MCP client.
Right. That client uses JSON RPC 2 .0, that text
-based standard, to talk to the MCP server, which
then interacts with the actual back -end service.
The whole path is designed around that text -based
semantic communication. Okay. Pretty direct.
Now contrast that with gRPC. The gRPC flow immediately
shows that extra component. You have the LM agent.
Then you have to insert that adapter layer, the
AI translation piece we just talked about. The
bridge. Before you get to the gRPC client, which
then talks to the gRPC service, likely using
protobuffs over HTTP2, that adapter adds a hop,
adds development time. Yeah. But it's the price
of admission for tapping into gRPC's speed. And
the discovery process, how an agent figures out
what it can do, also looks really different,
right? Totally different philosophies. With MCP's
built -in intelligence, discovery is, well, intelligent.
The agent sends tools lists and gets back those
nice semantic descriptions. I am the tool for
updating inventory or whatever. It's conversational.
Whereas with gRPC. GRPC offers something called
server reflection for technical discovery. But
what it returns is the raw technical protobuf
definition. It's like getting the engineer's
blueprint incredibly detailed about the structure.
But no instructions on when to use that part
of the blueprint. Exactly. It tells you what
the function signature is, but not the why or
the when in plain English. It lacks that semantic
layer inherently. So boiling it down, if you
ignore the AI understanding part for a second.
What's the fundamental performance trade -off
shown in these architectures? It really comes
down to a direct trade. Built -in AI semantic
intelligence with MCP versus highly optimized
binary speed with gRPC. Okay, let's put some
rough numbers on that performance difference.
This is where gRPC really shines, or at least
where the difference becomes stark. Right. MCP,
using JSON -RPC 2 .0, is text. It's human -readable,
AI -readable, but text is inherently kind of
verbose, right? A simple tool call might easily
be, say, 60 bytes, maybe more, once you include
the descriptions and JSON overhead. Okay, 60
-plus bytes. And gRPC with protobufs? Ruthlessly
efficient. That same logical request encoded
in binary protobuf. It might only be 20 bytes,
maybe even less depending on the specifics. Wow,
that's like a third of the size. Yeah, or even
smaller. And that size difference gets amplified
because gRPC uses HTTP2 multiplexing. Explain
that quickly. It means gRPC can handle many requests
and responses flying back and forth at the same
time over a single network connection. Think
of it like multiple conversations happening in
parallel on one phone line instead of need. a
new line for each call. Drastically cuts down
network chatter and latency. Whoa. Okay, imagine
scaling that. A billion queries a day using that
tiny binary format with tons of requests happening
simultaneously on each connection. The efficiency
gains, the cost savings. That's actually staggering.
It really is. Which leads us to the key takeaway.
Context determines the winner. There's no single
best protocol here. Right. So when does MCP make
the most sense? MCP excels when that AI discovery
piece is crucial, when the agent needs to dynamically
figure out what it can do, when semantic understanding
is really important for the task, maybe in more
complex conversational agents, and definitely
during rapid prototyping, where ease of use and
understanding is key, and maybe raw throughput
isn't the main concern yet. Okay, makes sense.
And the flip side, when is gRPC just dominating?
gRPC dominates when performance is absolutely
paramount. high -frequency trading systems, real
-time data streaming applications, anything where
milliseconds genuinely matter. Also, when you're
integrating with an existing backend that's already
built on microservices using gRPC, and of course,
at massive, massive scale. So given that performance
edge and its history, does that mean gRPC is
basically the default choice once enterprises
get serious about putting agents into production?
Well, gRPC is definitely the trusted choice for
that. core high -scale infrastructure, no question.
But MCP seems much better suited for that initial
AI agent development, especially when you're
building something new, AI first, and need that
flexibility and semantic understanding early
on. Which really brings us to the big idea, the
synthesis here. The sources are pretty clear
and it feels intuitive. We're heading towards
a hybrid future. Yeah, it's not really going
to be either in the long run, is it? Especially
as these agents get more sophisticated and handle
more critical, high volume tasks. You'll likely
need both. So how does that hybrid model look?
What role does each play? We're seeing MCP emerge
as the potential front door. It handles that
initial interaction, the semantic understanding,
the discovery, what should I do? It's the intelligent
routing layer. Okay, the thinking part. Right.
And then GRPC acts as the engine behind the scenes.
Once MCP figures out what needs to happen, if
it's a performance -critical task, it hands it
off to a highly optimized GRPC service to actually
execute it with maximum speed and efficiency.
The high -throughput workhorse. Exactly. So the
best practice seems to be maybe light quickly
with MCP. Build your agent. Get the logic right.
Then identify the real performance bottlenecks.
Which specific operations need that gRPC speed?
Implement gRPC just for those and build that
crucial translation layer carefully between your
MCP front end and your gRPC back end services.
Get the best of both worlds, but be deliberate
about it. You know, this whole MCP versus GRPC
thing, it feels like it represents a bigger question
in AI development, doesn't it? Yeah, it's a microcosm
of the broader debate. Do we adapt to the robust,
proven technologies we already have, like GRPC
from the microservices world? Or do we need to
build fundamentally new AI -native solutions
from scratch, like MCP? And the answer, like
always it seems, is probably... a bit of both.
It usually is. The key really looks like flexibility
and understanding that the choice you make early
on, MCP or gRPC or a mix, it doesn't just impact
performance today. It kind of sets the philosophy
for how your AI systems will evolve. That's a
great point. So here's maybe a final thought
to chew on. Can we eventually bake that gRPC
level efficiency into inherently semantic protocols
like MCP? Or will semantics and raw binary speed
always require that sort of adapter, that translation
layer sitting between them? That feels like a
really core architectural challenge for the future.
A fascinating question at the intersection of
AI concepts and hardcore infrastructure engineering.
Well, thank you for digging into the sources
with us today on this one. It's a really nuanced
and important area. Absolutely. Lots to think
about. Indeed. Thanks, everyone, for joining
us on the Deep Dive. We'll catch you next time.