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MCP vs gRPC: How AI Agents & LLMs Connect to Tools & Data

#AI Tools
2025-10-28 12:3211 min read

Content Introduction
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The video discusses the challenges faced by AI agents powered by large language models (LLMs) in connecting to external services like flight booking, inventory checking, and databases. It introduces two protocols: Model Context Protocol (MCP), which was created for AI agents to access tools and data, and gRPC, a widely-used framework for connecting microservices that excels in performance but wasn't designed with AI in mind. The video highlights MCP’s AI-native features, such as runtime discovery and the ability for LLMs to fetch external data dynamically, comparing it to gRPC’s binary serialization and streaming capabilities. While MCP allows for intuitive discovery and usage of tools, gRPC offers speed but requires additional layers for AI translation. Ultimately, the video suggests that as AI systems evolve, MCP will serve as a discovery interface, while gRPC will be utilized for handling high-throughput requests.

Key Information

  • AI agents powered by large language models (LLMs) face challenges in communicating with external services for tasks like booking flights or querying databases.
  • MCP (Model Context Protocol), introduced by Anthropic in late 2024, is designed specifically for AI agents to connect LLMs to tools and data.
  • gRPC (Google Remote Procedure Call) is a well-known RPC framework for connecting microservices but wasn't initially designed for AI applications.
  • Both protocols address challenges related to the context window limitations of LLMs, which cannot store all necessary information at once.
  • MCP allows LLMs to query external systems dynamically instead of trying to fit all data into a context window.
  • MCP includes primitives such as tools, resources, and prompts with natural language descriptions for easy comprehension by LLMs.
  • In MCP, discovery is built into the protocol, allowing AI agents to quickly understand available capabilities.
  • gRPC offers features like efficient binary serialization, bidirectional streaming, and faster performance, but provides structural information instead of semantic context required by LLMs.
  • An adapter layer is often necessary in gRPC implementations to translate natural language queries into specific RPC calls for AI agents.
  • MCP uses JSON-RPC 2.0 for communication, which is human-readable, while gRPC uses protocol buffers for binary communication, resulting in faster message parsing.
  • MCP is best suited for AI discovery, while gRPC excels in speed and efficiency for high-throughput workloads.

Timeline Analysis

Content Keywords

AI Agents and Communication Protocols

AI agents, powered by large language models (LLMs), require effective communication with external services for tasks like flight bookings and inventory checks. The discussion focuses on two protocols—Model Context Protocol (MCP) and Google Remote Procedure Call (gRPC)—and their roles in enhancing agentic AI capabilities.

Model Context Protocol (MCP)

Introduced by Anthropic, MCP is designed for AI agents facilitating connections between LLMs and external tools and data. It offers primitives such as tools, resources, and prompts, enabling dynamic querying and runtime discovery of capabilities.

gRPC (Google Remote Procedure Call)

gRPC is a fast and reliable RPC framework that organizes communication between microservices. It uses protocol buffers for efficient binary serialization and real-time streaming. However, it lacks the semantic context needed for LLMs, which often necessitates an additional AI translation layer.

Context Window Limitations in LLMs

LLMs face limitations due to their context window, which restricts the amount of information they can retain. MCP allows these models to query external systems for real-time data, overcoming the constraints of large datasets or databases.

Adaptability of AI Agents

MCP promotes adaptability in AI agents, enabling them to leverage new tools and capabilities without retraining. This flexibility contrasts with gRPC, which typically requires specific knowledge of service details.

Communication Flow in MCP and gRPC

The communication processes for MCP and gRPC differ fundamentally. MCP uses JSON-RPC for connections, which allows natural language interaction, while gRPC utilizes HTTP/2 with protocol buffers, offering faster binary communication but requiring an adapter for AI-friendly intents.

Discovery Mechanisms

MCP integrates discovery into its protocol, enabling AI agents to access available tools and resources dynamically. In contrast, gRPC provides only structural information, necessitating an adapter layer for broader understanding and use.

Efficiency and Performance Comparison

MCP is designed for AI, providing semantic clarity, while gRPC excels in speed and scalability. The choice between both protocols will depend on the specific application requirements as AI agents evolve from simple chatbots to robust production systems.

Related questions&answers

What is MCP?

MCP, or Model Context Protocol, is a purpose-built protocol developed by Anthropic in late 2024 for AI agents to connect large language models (LLMs) to tools and data.

How does gRPC differ from MCP?

gRPC is a remote procedure call framework that offers speed and reliability but was not designed with AI in mind. It requires an additional AI translation layer, while MCP is specifically built for AI, providing semantic context and runtime discovery.

What are the limitations of LLMs?

LLMs are fundamentally limited by their context window and the training data they were exposed to. They cannot hold entire customer databases or real-time data feeds directly in their context.

How do AI agents overcome LLM limitations?

AI agents can query external systems on demand for specific information, enhancing their capabilities without overloading their context window.

What are the key components of the MCP protocol?

MCP provides three key primitives: tools (functions like 'get weather'), resources (data like database schemas), and prompts (interaction templates), all with natural language descriptions for LLMs.

What does the communication flow look like in MCP?

In MCP, the flow starts with a host application connecting to an MCP client, which then interacts with an MCP server that connects to external services, with results returned back through the same path.

How does gRPC handle requests?

gRPC utilizes HTTP/2 for communication, allowing bidirectional streaming and multiplexing, which enables multiple requests to be handled simultaneously.

What is the discovery mechanism in MCP?

MCP has built-in discovery, allowing clients to connect to an MCP server and immediately call commands like tool/list or resources/list to understand available capabilities in a semantic manner.

Why do developers need an AI translation layer for gRPC?

Developers need an AI translation layer because gRPC provides structural information through protobuf definitions rather than semantic context, requiring an additional step for translating natural language intent into specific RPC calls.

How do MCP's and gRPC's efficiencies compare?

MCP uses JSON-RPC 2.0 with text-based messages, making it easier to read but often more verbose. In contrast, gRPC uses binary protocol buffers, which are faster and smaller but less human-readable.

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