MCP Protocol: Does It Really Solve AI Agent's Tool Calling Problem?

One technical term keeps popping up in AI circles this year: MCP, short for Model Context Protocol.

Anthropic’s open-source protocol aims to establish a standardized interface between AI agents and external tools. Sounds wonderful—whether you’re querying databases, checking weather, or controlling browsers, one protocol to rule them all. No more writing different adapter code for each tool.

But after several months of usage, I’ve found it’s not that simple.

Let’s start with the positives. MCP’s design philosophy is sound. Traditional Function Calling enables model tool usage, but each platform has its own implementation—OpenAI, Google, and Anthropic are mutually incompatible. If your agent needs cross-platform deployment, you maintain three codebases. MCP attempts to solve this with a ‘write once, run anywhere’ standard.

The architecture is cleverly designed too. It encapsulates tool definitions, permission management, and execution flows within the protocol. Developers only implement the server side; clients can reuse existing libraries. Tools like Serena—an MCP-based AI programming IDE—already demonstrate the protocol’s potential in complex scenarios.

But—and there’s always a but—MCP’s current ecosystem remains fragile.

First problem: coverage. While Anthropic is pushing hard, OpenAI and Google haven’t explicitly committed to support. If these giants don’t get on board, MCP stays an ‘Anthropic ecosystem standard’ rather than achieving true industry-wide adoption.

Second problem: tool quality. MCP itself is just a communication specification; actual tool implementations vary wildly in quality. Some tool descriptions are vague and hard for models to interpret; others return inconsistently formatted data requiring extra cleaning logic. These issues exist at the implementation level, beyond what any protocol can solve.

The third problem runs deeper: tool calling’s fundamental challenge isn’t ‘how to call’ but ‘when to call.’

Today’s LLMs handle ‘what the user wants’ reasonably well, but still struggle with ‘which tools do I need, in what order, to complete this task.’ This isn’t a protocol issue—it’s a model reasoning capability problem.

Example: a user says ‘check tomorrow’s weather in Beijing and remind me to bring an umbrella if it rains.’ This requires two tools—weather lookup and reminder setting—but more importantly, the model needs to understand conditional logic (‘if…then…’) and arrange execution accordingly.

MCP can standardize the calling process, but it can’t make the model smarter.

I’ve noticed a trend: many agent projects are ‘stacking tools’—connecting as many as possible, as if quantity equals power. This is actually a misconception. Truly useful agents typically use a carefully curated handful of tools, but use them exceptionally well.

From another angle, MCP’s rise reflects industry consensus: AI applications are shifting from ‘model-centric’ to ‘tool-centric.’ Future competitive advantage may depend less on whose base model is stronger, and more on who can build the richest, most reliable tool ecosystem.

My advice for developers: experiment with MCP, but don’t bet everything on it. Maintain familiarity with traditional approaches like Function Calling, choosing the right technical path for each scenario. And pay close attention to tool quality—a good tool with a less ‘standard’ interface beats a standard but poorly designed one.

One final thought: protocol battles are often more complex than the technology itself. Whether MCP becomes an industry standard largely depends on Anthropic’s ability to convince other players to join. This isn’t a technical problem—it’s business negotiation.

Do you think MCP will become an industry standard?