The inherent limitations of large language models—constrained by their context windows and static training data—present a fundamental challenge for the burgeoning field of agentic AI. How, then, do these text-based intelligences reliably communicate with and leverage external tools and dynamic data services? Martin Keen, a Master Inventor at IBM, presented a crucial discussion addressing this very question, delineating the distinct approaches of Model Context Protocol (MCP) and gRPC in enabling smarter integrations for modern AI systems.
Keen highlights that even LLMs with expansive context windows, such as those capable of handling 200,000 tokens, cannot possibly encompass an entire customer database, a complete codebase, or real-time data feeds. The solution, he explains, lies in empowering AI agents to act as orchestrators. "The agentic LLM becomes something of an orchestrator, intelligently deciding what information it needs and when to fetch it." This capability allows LLMs to query external systems on demand, pulling in relevant data from CRM tools, weather APIs, or databases as required, rather than attempting to hold all potential knowledge within their immediate context.
The Model Context Protocol (MCP), introduced by Anthropic in late 2024, stands out as an AI-native solution. It is purpose-built for AI agents to connect LLMs to tools and data, offering three core primitives: Tools, Resources, and Prompts. Tools represent executable functions, such as `getWeather`; Resources are data structures like database schemas; and Prompts are interaction templates. Critically, these primitives are accompanied by natural language descriptions, making them inherently understandable to LLMs. This design facilitates "runtime discovery," allowing an AI agent to dynamically query an MCP server with a `tools/list` command and receive human-readable descriptions of available functionalities. This empowers agents to adapt to new capabilities without requiring costly and time-consuming retraining.
In contrast, gRPC, or Google Remote Procedure Call, is a robust, well-established RPC framework that has been connecting microservices for nearly a decade. While offering fast performance, bi-directional streaming, and code generation through Protocol Buffers for efficient binary serialization, gRPC was not originally designed with AI in mind. Consequently, it provides structural information rather than the semantic context that LLMs need to understand *when* and *why* to use a service. This necessitates an additional "AI Translation" or adapter layer between the AI agent and the gRPC client, bridging the gap between natural language intent and specific RPC calls.
Architecturally, the protocols diverge significantly in their communication mechanisms. MCP clients communicate with an MCP server via JSON-RPC 2.0, which relies on text-based messages. These messages are human-readable and, crucially, LLM-readable, simplifying debugging and direct interpretation by AI models. A simple tool call in MCP might look like a JSON object: `{"method": "getWeather", "params": {"city": "Boston"}}`. This text-based verbosity, however, comes at a cost.
gRPC, conversely, utilizes HTTP/2 with Protocol Buffers for communication, resulting in binary messages. These binary messages are significantly smaller and faster to parse than their text-based counterparts. For instance, the same weather request might be 20 bytes in gRPC compared to 60-plus bytes in JSON. Furthermore, HTTP/2 enables multiplexing, allowing multiple requests over a single connection, and robust streaming capabilities for real-time data flows. While MCP sends one request and awaits a response, gRPC can fire off dozens of parallel requests or maintain open data streams, offering superior throughput.
Related Reading
- Anthropic's MCP: The Universal Connector for Agentic AI
- Dissecting AI Agent Architectures: Beyond Simple Loops
- OpenAI Unveils AgentKit: Democratizing Agent Orchestration for Enterprises
The choice between MCP and gRPC ultimately boils down to a trade-off between semantic understanding and raw performance. For applications like chatbots handling a modest number of requests per second, MCP's text-based overhead is negligible, and its AI-native design offers direct benefits in agent intelligence and adaptability. However, for agents processing thousands of requests per second, where every millisecond counts, gRPC's speed and efficiency become critical.
MCP was born in the age of AI. It's built to help LLMs and agents understand what tools do and when to use them. As AI agents mature from conversational chatbots to complex production systems, expect to see both protocols play complementary roles. MCP will likely serve as the front door for AI discovery, enabling agents to dynamically comprehend and adapt to new functionalities. Meanwhile, gRPC will continue to be the engine for high-throughput workloads, ensuring the rapid and scalable execution of tasks once the agent has intelligently determined the appropriate course of action.
