MCP Is More Useful as Context Distribution Than as RPC
The article, published on DEV Community, presents the thesis that the Model Context Protocol (MCP) is more effectively utilized for context distribution rather than as a remote procedure call (RPC) framework. The author contends that MCP's design and typical use cases align better with sharing contextual data among AI models and applications, as opposed to facilitating direct, synchronous function calls. The piece does not provide specific technical benchmarks, version numbers, or implementation details, but focuses on the conceptual distinction between context distribution and RPC. It implies that developers may achieve greater value by leveraging MCP to propagate relevant information across systems, thereby enhancing AI model performance and coherence, rather than treating it as a standard RPC tool. The article does not mention any particular models, tools, or real-world deployments, and remains at a high-level discussion of MCP's intended utility.
Developers should consider MCP for context sharing rather than RPC to maximize its utility.