MCP Protocol Explained: The Standardization Revolution in LLM Tool Calling

What Is MCP?

MCP, short for Model Context Protocol, is an open communication protocol proposed by Anthropic (the company behind the Claude model series). Its core mission is singular: to establish a unified standard for integrating large language models with external tools.

As major players race to dominate the AI productivity space, the MCP ecosystem is expanding rapidly. Domestic cloud providers in China — including Baidu Cloud, Tencent Cloud, and Volcengine — have opened their own "MCP Marketplaces" and integrated them into desktop AI applications. This signals that MCP has evolved from a technical concept into foundational infrastructure connecting LLMs with real-world capabilities.

In simple terms, MCP is the "universal adapter" for large language models — regardless of which model you use, as long as it follows this protocol, it can seamlessly plug into third-party tools and services.

Architecturally, MCP adopts a Client-Server model. The MCP Host (the AI application, such as Claude Desktop or WorkBuddy) initiates requests; the MCP Client is the protocol client embedded within the host, maintaining connections to servers; and the MCP Server is a lightweight service process that exposes specific tool capabilities. All three communicate via standardized JSON-RPC 2.0 message format, supporting both local process (stdio) and remote HTTP/SSE transport methods. This layered design means tool providers only need to implement one MCP-compliant Server to be discovered and invoked by any MCP-supporting AI application — no need to build separate integrations for each model vendor.

The Current State of the MCP Ecosystem

The MCP ecosystem is already quite vibrant. Take WorkBuddy, Tencent's consumer-facing desktop productivity app — it ships with a fully integrated MCP Marketplace. Within its "Expert" module, alongside various Agents and Skills, there's a key "Connector" feature. These connectors are, in essence, the real-world embodiment of the MCP protocol.

Users can connect third-party services — such as KV Visualization — to the AI Coding interface simply by scanning a QR code to authorize. Once authorized, users can call that service's API directly from the client, achieving true plug-and-play tool integration.

Beyond vendor-built marketplaces, platforms like ModelScope have established their own MCP Marketplaces, hosting a wide range of services from third-party ecosystem companies — from browser automation to OpenMemory by mem0, a long-term memory-focused MCP service.

Traditional Enterprises Monetizing Capabilities via MCP

You may not have noticed, but a growing number of traditional enterprises are packaging their internal or client-facing capabilities as API endpoints, then wrapping them as "Connectors" via the MCP protocol. This allows ordinary users to pay a small API usage fee to access these MCP services and accomplish complex tasks that were previously out of reach. In practice, this opens an entirely new monetization channel for traditional enterprises.

Why Was MCP Created?

To appreciate MCP's value, you need to understand the context in which it was born. Traditional large language models have two inherent limitations: frozen knowledge and inability to act.

Frozen knowledge means the model's information is locked to a specific point in time after training is complete. Inability to act means the model can "only talk, not do." Both problems were being addressed even in the era of Function Calling.

How Function Calling Works

Function Calling works by having the LLM generate a structured instruction — typically in JSON format — known as a tool call message. The model extracts the parameters required by a downstream API from the user's conversation, fills them into this message, and the code then executes the corresponding API call. The result is returned to the model, which uses it to generate a final response.

This mechanism allows LLMs to indirectly interact with external API tools. However, while Function Calling solved the "frozen knowledge" and "inability to act" problems, it introduced a thornier new issue: fragmentation.

Function Calling was first formally introduced by OpenAI in June 2023 alongside the GPT-3.5/GPT-4 API update, and quickly became the embryonic industry standard. The core flow has four steps: developers declare available tools' names, descriptions, and parameter schemas upfront; the model determines whether a tool call is needed and, if so, outputs a structured JSON containing the tool name and parameters (the tool call message) instead of directly answering; the calling code intercepts this message and executes the actual API request; and finally, the result is returned to the model as a tool result message, from which the model generates its final reply. This mechanism transformed models from "talking heads" into "systems that can direct external actions" — a pivotal step in the evolution from conversational assistant to AI Agent.

The Three Core Pain Points MCP Addresses

Pain Point 1: No Unified Standard Across Model Vendors

Different model vendors each have their own Function Calling standard. Anthropic's Claude uses its own tool definition schema; Alibaba's Qwen uses a completely different one — even the invocation patterns differ.

Consider a realistic scenario: a developer writes a large codebase using Claude, with tool definitions fully aligned to Claude's standard.

Then, for cost-cutting reasons, the company decides to switch to Qwen. Because Qwen's tool definition format is entirely different from Claude's, the developer is forced to rewrite the entire toolset from scratch to accommodate the new model. This "rewrite every time you switch models" dilemma is the first problem MCP aims to solve: tool definitions need to be compatible across different models.

Pain Point 2: Inconsistent Tool Quality Across Implementations

The second problem arises within teams. Suppose Developer A and Developer B both implement a getWeather tool, but each writes their own prompt (Prompt A and Prompt B). The result: the same functionality, defined by two different people, ends up with vastly different sensitivity and invocability.

It's like a famous restaurant where Chef A and Chef B each make sweet and sour pork — but the taste is completely different. When a company offers both of these "same-name, different-quality" tools to external clients, customers will naturally ask: why does the same capability behave inconsistently? This points to the second core need: tool capabilities need to be standardized.

Pain Point 3: Tool Code Cannot Be Reused or Shared Across Projects

The third pain point is reusability. When Project A defines a tool, Project B may need the same tool. But since Function Calling logic is hardcoded, the developer working on Project B has no choice but to copy the code from Project A and rewrite the calling flow — extremely inefficient.

Even a single developer reusing tools from a previous project has to copy code repeatedly across projects. The ideal state should work like calling an API: build once, share with everyone. Once a tool is defined, any developer across the entire team — or even the entire organization — should be able to call it directly with consistent results. This is MCP's third goal: tool capabilities need to be reusable and shareable.

Conclusion: MCP as the Foundational Protocol for AI Tool Ecosystems

At its core, MCP is an open protocol that re-standardizes the fragmented tool-calling ecosystem of the LLM era. It addresses both the age-old problems of "frozen knowledge and inability to act," and the three major engineering pain points that emerged from Function Calling: inconsistent standards across model vendors, uneven tool quality across implementations, and difficulty reusing code across projects.

From a technical evolution standpoint, MCP is to the AI tool ecosystem what USB is to peripherals and HTTP is to the internet — it gives "connecting LLMs to everything" a reliable, universal, and extensible engineering foundation for the first time. For developers and enterprises alike, understanding and positioning within the MCP ecosystem early is undoubtedly a critical step toward embracing the next generation of AI application development.

It's worth noting that MCP is still in a rapidly evolving phase. The specification is led by Anthropic but maintained openly on GitHub. Unlike mature protocols such as HTTP or USB, MCP still has room to improve in areas like security (e.g., tool permission sandboxing, protection against malicious Servers), large-scale concurrency performance, and cross-language SDK completeness. The community is also debating whether MCP can truly remain neutral across model vendors — after all, the direction of the protocol's evolution is significantly influenced by Anthropic. While embracing the MCP ecosystem enthusiastically, developers should also be mindful of compatibility risks that come with spec updates, and it's advisable to explicitly pin protocol versions in mission-critical applications.