What This Is
This article (source: Juejin Tech Blog) tackles a specific problem: after an AI model says "I want to call tool A with these parameters," how does the program actually execute the call, then return the result to the model so the model can compose a reply for the user.
It splits the workflow into three roles: the model proposes the call (Function Calling — the model's ability to embed "I want to use this tool, with these parameters" inside its answer); the application handles parsing, routing, and re-requesting the model; and MCP (Model Context Protocol, the connection protocol Anthropic open-sourced in November 2024) defines the format applications and tool services use to talk to each other. MCP is neither the mechanism that generates the call, nor the code that executes the tool — it is only the middle layer. The latest specification, released in June 2025, has already been adopted by major players including OpenAI.
The article's methodological core is keeping three things distinct: the tool declaration (the rule), this call's parameter values, and the execution result. It walks through a minimal closed loop using a "read a local file" example, repeatedly emphasizing: writing "path is a string" in the rule won't auto-magically find the file; the model guessing a wrong parameter isn't "autocomplete."
Industry View
Supporters' logic is direct: standardization breeds ecosystems. USB-C for hardware, HTTP for the web — MCP for AI tool-calling is the same kind of story. Once the protocol is unified, tool builders no longer need to adapt one by one for each model; "build one tool, sell to every model" becomes possible. That is the core driver behind MCP's rapid acceptance across vendors in 2025.
But there are cooler heads. Anthropic and OpenAI are both protocol originators and platform owners. When MCP decides who gets listed in the "tool store" and what permissions they receive, enterprises face a new dependency — much like today's reliance on iOS and Android. A more concrete counter-example: OpenAI itself retains a large set of "platform-hosted tools" that do not all flow through MCP. The real moat likely sits in the application layer and data assets; the protocol layer is just distribution rights.
An easily overlooked risk hides inside the article: protocol errors and tool execution errors must be handled differently. Mistaking "tool failed" for "succeeded" will cause enterprise Agent trust to collapse fast after launch. This is the most common early-stage accident type we have seen.
Impact on Regular People
For enterprise IT: A new item will appear on vendor evaluation checklists — "Does it support MCP?" That means future model swaps or frontend changes won't require rewriting tool integrations. Choice expands, but so does lock-in.
For individual careers: You don't need to understand MCP, but from the second half of this year you'll notice your AI assistant "actually doing things" more often — reading files, querying internal systems, editing spreadsheets. This connection spec is what powers that.
For consumer markets: A side effect of standardization is that "AI buttons" inside every app will multiply and get better. But once AI gains read/write access to your files, permission auditing and data governance will become new household and enterprise topics.