When AI agents powered by large language models need to book a flight or check inventory or just query a database, they face a fundamental problem. How does a text-based AI reliably communicate with these external services? Well, two protocols can help. One of those is MCP, that's Model Context Protocol. It was introduced by Anthropic in late 2024, and it's purpose-built for AI agents for connecting LLMs to tools and to data. The second thing that might be able to help is gRPC, that's Google Remote Procedure Call. And that's a well-used RPC framework that's been connecting microservices for nearly a decade, offering really fast performance. But it wasn't designed with AI in mind. So, the question is: How do MCP and gRPC address the needs of agentic AI? Well, LLMs, they're fundamentally limited by something and that is their context window. This is all of the things that an LLM can kind of keep in mind at once. And they're also limited by what they were trained on, by their training data. these two things kind of limit what an LLM can do.
And even LLMs with really big context windows, let's say this one is 200 K, that still can't fit everything. It can't fit like an entire customer database or a code-based or real-time data feeds. So instead of cramming everything into context, we give LLMs the ability to query external systems on demand. So, let's say you need some customer data. Well, you could query a CRM tool and add that into the context window. Or maybe you need the latest weather data. Well, you could call the weather API and the agentic LLM becomes something of an orchestrator, intelligently deciding what information it needs and when to fetch it. Now, MCP approaches this challenge as an AI-native protocol, and it provides three primitives. So, one of those primitives is called tools. So that's functions like 'get weather', for example.
Another primitive is called resources. That might be data like database schemas. And then the third is prompts. So we're thinking along the lines of kind of interaction templates. And all of these are with natural language descriptions that LLMs can understand. So, when an AI agent connects to an MCP server, it can ask 'Hey, what can you do?' And it does that via the tools/list command and gets back human-readable descriptions. Like, hey, this tool reports whether use this tool when users ask about temperature. So it's really built specifically around the concept of runtime discovery, of being able to find the right tool at the right time. Agents can adapt to new capabilities without being retrained. Now, gRPC takes a different approach, offering protocol buffers for efficient binary serialization, bidirectional streaming for real-time communication, and code generation. It's fast, reliable and it's proven at scale, but gRPC provides structural information rather than the semantic context that LLMs need to understand the when and the why of how to use the service. So developers might actually need to add an extra step here called AI translation into the mix. And that is kind of a layer on top. And that's because generic protocols like gRPC, they were designed for deterministic systems where the caller knows exactly what to call and when. AI agents, they're probabilistic, they need to understand not just the how, but the what, the when and the why of each tool. Now let's take a look at the architectural components and how they communicate. So in the MCP world, you might have at the top here a host application that manages one or more MCP clients. And each client, it opens a connection using a protocol called JSON-RPC 2.0.
And that goes to an MCP server, and the server that actually wraps actual capabilities. So maybe it gives us access to a ... to a database, or maybe it goes to a API, or maybe it goes to a file system.
Now, the communication flow here is we start at the host, and we go to the MCP client, which goes to the server, which goes to the external service, and then the results go all the way back again. Now in the gRPC ecosystem, we're going to start here with an AI agent. And that uses a gRPC client that makes direct calls using the protocol of HTTP/2 with protocol buffers.
And I'll talk a bit more about those in a moment. And that all goes to gRPC services. Now these services, they expose methods that the AI can invoke. But this isn't a complete picture because you typically need an adapter layer in the middle here, between the AI agent and the client, to translate natural language intent into specific RPC calls. So the flow here is actually AI agent into the adapter layer, which goes to the gRPC client, which goes to the gRPC service. And the discovery mechanisms are quite different with these as well. So with MCP, discovery is built into the protocol. When an MCP client connects to a server, it can immediately call tool/list or resources/list or prompts/list to understand the available capabilities. And these are more than method signatures; they actually include the natural language descriptions that are designed for LLM consumption. The server might advertise, let's say, ten different tools and each includes guidance like use this tool for weather queries or call this one when the user asks for financial data. The AI agent can dynamically adapt to what's available. gRPC offers server reflection. You can query what services and methods exist, but you get protobuf definitions, not semantic descriptions. So, a weather service that shows a 'get weather' method signature, but it doesn't explain when or why to use it. That's where the adapter layer comes in. But gRPC does hold an advantage when it comes to speed, and that's because of differences in transport. Now, I already mentioned that MCP uses JSON- RPC 2.0. That means that it is text-based messages. These are messages that are human-readable and also LLM-readable. And a simple tool call, it might look something like this, it's easy to read and debug, but yeah, it's verbose. Now gRPC, that instead uses protocol buffers for communication. And those aren't text-based; they are binary. And that makes messages a good deal smaller and faster to parse. The same whether request in gRPC, that might be like 20 bytes versus 60+ when we're talking about JSON. But it's not just size. gRPC that runs over HTTP/2, which enables multiplexing, meaning multiple requests on one connection, streaming as well, meaning a ... a real-time data flow. So, while MCP sends one request and kind of waits for a response, gRPC can fire off dozens of parallel requests or maintain an open stream of data. So for a chatbot handling a few requests per second, meh, MCP's overhead is not a big deal. For an agent processing thousands of requests, well, those milliseconds add up.
Basically, it comes down to this: MCP was born in the age of AI.
It's built to help LLMs and agents understand what tools do and well, when to use them; gRPC, that brings proven speed and scale from the microservices world, but it needs translation layers to kind of speak AI. So as agents mature from chatbots to production systems, expect to see both: MCP as the front door for AI discovery, gRPC as the engine for high throughput workloads.
