MCP (Model Context Protocol): Solving the Three Core Pain Points of AI Tool Calling

Introduction: Starting from the Dilemma of Tool Calling

MCP (Model Context Protocol) is one of the hottest topics in AI development today. However, with so many tutorials jumping straight into concepts and development, many developers haven't truly understood: What problem does MCP actually solve? Why does it deserve so much attention?

This article starts from the core pain points of Agent development, helping you build a comprehensive cognitive framework for MCP and understand why it has become the standardized solution for AI tool calling.

The Three Elements of an Agent: Prerequisites for Understanding MCP

LLM + Prompts + Tools

An AI Agent's core consists of three elements:

• LLM (Large Language Model): Provides reasoning and generation capabilities; ordinary developers cannot directly intervene in its internal mechanisms
• Prompt: The core skill that AI application developers need to master, determining the Agent's behavioral patterns
• Tools: Typically developed by third parties, giving the Agent the ability to interact with the external world

The concept of AI Agent originates from a classic theory in artificial intelligence — Intelligent Agent, referring to an autonomous entity capable of perceiving its environment, making decisions, and taking actions. In the era of large models, Agent specifically refers to an application architecture that uses an LLM as its reasoning core, combined with tool calling, memory management, and planning capabilities. Since 2023, projects like AutoGPT, BabyAGI, and LangChain Agent have popularized this paradigm. Unlike traditional chatbots, Agents emphasize closed-loop action capability — they can not only think but also execute.

The relationship among these three is: the LLM uses developer-written prompts (including Function Calling or Tool Calling descriptions) to decide when and how to invoke tools. It's precisely the addition of tools that upgrades a pure LLM into an Agent with action capabilities.

The Essence of Tool Calling

You might not have noticed, but Tool Calling is essentially a form of Prompt — it uses structured tool descriptions to tell the LLM what tools are available, what each tool does, and what parameters are required. This description organically combines the three elements of an Agent.

Tool Calling (also known as Function Calling) was first officially introduced by OpenAI in June 2023 alongside the GPT-3.5/GPT-4 API. Its core mechanism is: developers describe available functions' names, parameter types, and purposes in JSON Schema format within API requests, and the LLM determines during reasoning whether to call a function and generates structured call parameters. It's important to note that the model itself doesn't execute functions — it returns a call intent, and the application layer code actually executes it and passes the result back to the model for continued reasoning. This design upgrades the LLM from a pure text generator to the core of an intelligent agent capable of interacting with external systems.

The Three Pain Points of Tool Calling

Although Tool Calling is a critical component of building Agents, it has always had three frustrating problems in practice:

Pain Point 1: Verbose Descriptions

To make an LLM correctly understand and invoke a tool, developers need to write very detailed tool descriptions, including functionality explanations, parameter definitions, return value formats, and more. This process is both time-consuming and error-prone, especially as the number of tools grows and maintenance costs skyrocket.

Take a simple weather query tool as an example — developers need to define the function name, functionality description, type and meaning of each parameter, required vs. optional fields, enum value ranges, etc. When an Agent needs to integrate dozens of tools, these description texts alone occupy a significant portion of the Context Window, not only increasing Token consumption costs but also potentially causing the model to "lose focus" due to excessive descriptions, reducing call accuracy.

Pain Point 2: Unstable Invocations

Even with well-written tool descriptions, the LLM's actual calling behavior during runtime is not stable enough — sometimes it doesn't call a tool when it should, and sometimes it triggers one when it shouldn't. This uncertainty severely impacts Agent reliability.

The root cause of this instability lies in the probabilistic generation nature of LLMs. LLMs generate output by predicting the next Token, and tool calling decisions are part of this probabilistic process. The Temperature parameter, context length, and subtle changes in prompts can all affect whether the model triggers a tool call. In production environments, this means the same user request might produce different behaviors at different times, posing serious challenges for business scenarios requiring deterministic output (such as financial transactions or medical advice).

Pain Point 3: Lack of Unified Standards

Different LLMs have different format requirements for Tool Calling descriptions. OpenAI has its own format, Anthropic has its own, and DeepSeek has yet another set of requirements. If developers want the same tool to work across multiple models, they need to write multiple sets of descriptions — clearly not a sustainable approach.

Specifically, OpenAI requires tool descriptions in the tools field, wrapped with a function type; Anthropic's Claude uses a different Schema structure; Google's Gemini has its own function_declarations format. What's worse, different models vary in their support for parameter types, handling of nested objects, and error return formats. This fragmented state is like the early days of phone chargers — every manufacturer had its own standard, and users (developers) suffered greatly.

How MCP Solves These Problems

The Core Value of a Standardized Protocol

MCP was born precisely to address the challenges described above. Proposed by Anthropic, it aims to establish a unified standard protocol for interactions between LLMs and external tools and data sources.

Anthropic was founded in 2021 by former OpenAI Research VP Dario Amodei and Daniela Amodei as an AI safety company, with its flagship product being the Claude series of LLMs. In November 2024, Anthropic officially released the MCP open-source specification. There's a deeper logic behind why Anthropic, rather than OpenAI, is driving this standard: Anthropic has always emphasized AI safety and controllability, and MCP's design incorporates permission control and security boundary considerations. Additionally, as a second-tier player in the industry, promoting open standards helps break OpenAI's ecosystem monopoly — similar to Google's logic in promoting Android against iOS.

By analogy, MCP is to AI tool calling what HTTP is to web communication and USB is to hardware connections — it provides a universal "interface specification" that allows tool developers to build a single MCP Server following one standard, which can then be directly called by all MCP-compatible clients (such as Claude, Cursor, Dify, etc.).

The history of technical standardization repeatedly proves that unified protocols can unleash tremendous ecosystem value. HTTP (1991) allowed any browser to access any website; USB (1996) freed peripheral manufacturers from customizing interfaces for every computer; OAuth 2.0 (2012) enabled third-party applications to securely access user data. The common characteristic of these standards is: they reduced integration costs for ecosystem participants and shifted competition from the interface layer to the value layer. MCP is playing a similar role in the AI tool ecosystem — when tool developers only need to implement an MCP Server once to be callable by all compatible clients, the tool ecosystem will flourish exponentially.

MCP's Technical Architecture

MCP adopts the classic Client-Server architecture, with communication based on the JSON-RPC 2.0 protocol. The entire system includes three core roles:

• MCP Host (Host Application): Such as Claude Desktop, Cursor IDE, etc. — the application that users directly interact with
• MCP Client (Protocol Client): Responsible for establishing one-to-one connections with Servers and handling protocol-layer communication details
• MCP Server (Tool Server): Exposes specific Tools, Resources, and Prompt templates

The transport layer supports two modes: stdio (standard input/output, suitable for local process communication with fast startup and low latency) and HTTP+SSE (Server-Sent Events, suitable for remote services supporting cross-network calls). This layered design means tool developers don't need to worry about the specific implementation of upper-layer applications — they just need to follow the protocol specification. Beyond exposing tools, MCP Servers can also provide Resources (such as files, database content) and Prompt templates, enabling LLMs to access richer contextual information.

MCP's Relationship with the Existing AI Development Ecosystem

MCP is not meant to replace existing AI development tools and platforms, but rather to complement them:

• Relationship with Dify: As a workflow orchestration platform, Dify already supports MCP integration — the two are complementary, not competitive
• Relationship with tools like Nambo: They also support the MCP protocol and can integrate seamlessly
• Relationship with various LLMs: MCP provides a model-agnostic tool description standard, reducing cross-model adaptation costs

Dify is an open-source LLM application development platform that provides visual workflow orchestration, RAG (Retrieval-Augmented Generation) pipelines, Agent building, and model management capabilities. Its core value lies in enabling developers without deep technical backgrounds to quickly build AI applications. Dify began supporting MCP protocol integration in late 2024, allowing users to directly call tools provided by MCP Servers within workflow nodes without manually writing Tool Calling JSON descriptions. This integration approach significantly lowers the barrier to tool integration and validates MCP's ecosystem compatibility as a universal standard.

The Right Path to Learning MCP

Use It First, Then Develop

The problem with many MCP tutorials is that they start by teaching MCP Server development. The correct path should be — first experience MCP's value as a user, and once you find it useful, then learn how to develop your own MCP Server.

The recommended learning path is:

1. Understand the concepts: Know what MCP is and what problems it solves
2. Experience using it: Actually use existing MCP Servers in MCP-compatible clients (such as common MCP Servers for file system operations, database queries, web search, etc.)
3. Dive into the architecture: Understand MCP's technical architecture and communication mechanisms, including JSON-RPC message formats, Capability Negotiation flows, etc.
4. Hands-on development: Develop your own MCP Server based on your needs; official SDKs are available in TypeScript and Python
5. Explore the ecosystem: Discover the rich MCP Server resources in the open-source community; hundreds of open-source MCP Servers on GitHub already cover various scenarios

A Rapidly Iterating Ecosystem

It's worth noting that the MCP ecosystem is currently in a phase of rapid iteration, with both the protocol itself and surrounding tools being continuously updated. In early 2025, the MCP specification has evolved multiple times from its initial version, adding important features like Streamable HTTP transport and OAuth 2.1 authentication. When learning, you should refer to the latest official documentation (modelcontextprotocol.io) while keeping an eye on community developments. Currently, thousands of MCP Servers are shared within the community, covering a wide range of scenarios from database operations and cloud service management to code execution and knowledge retrieval.

Conclusion

The emergence of MCP is essentially the inevitable trend of the AI tool ecosystem moving from "fragmentation" to "standardization." It solves not only the technical pain points of Tool Calling but is also building the infrastructure that enables AI Agents to interact with the external world safely, stably, and efficiently.

From a broader perspective, MCP's significance lies in defining the "application layer protocol" for the AI era. Just as TCP/IP defined how computers communicate and HTTP defined how browsers fetch web pages, MCP is defining how AI models interact with external tools and data. Once this standard is widely adopted, we'll see a truly interconnected AI tool ecosystem — any tool built by any developer can be called by any AI application, which will greatly accelerate the pace of AI application innovation.

For AI application developers, understanding and mastering the MCP protocol is an important step from "toy-level demos" to "production-grade applications." Whether you're just getting started with Agent development or already using Tool Calling in production environments, MCP is worth your deep exploration.