What Is MCP and How It Revolutionizes AI Tool Integration

MCP Overview

MCP (Model‑Center Protocol) is a client‑server protocol that lets an AI agent invoke external tools in a standardized way.

Why MCP Is Needed

Solving the M×N Integration Nightmare

Before MCP, each AI application had to be hard‑coded to each external tool, requiring M × N integration modules for M applications and N tools. MCP introduces a universal protocol—analogous to the USB‑C standard—so each AI app implements an MCP client once and each tool implements an MCP server once, unlocking the whole ecosystem with a single integration.

Standardising Tool Discovery and Use

Standardised Interface: Provides a unified framework for seamless interaction between LLMs and tools.

Tool Discoverability: Defines a common description format, hosting model and exposure method, allowing LLMs to automatically discover and understand tool capabilities.

Reliability and Executability: Guarantees that tools are reliable, discoverable and executable, and adds built‑in approval and audit workflows.

Extending AI Capability Boundaries

Large language models are powerful but not omnipotent; many tasks require external operations. MCP expands model capabilities by enabling tool calls for data fetching and complex actions.

How MCP Works

Core Roles

User: Issues the original request.

MCP Host (client, e.g., Cursor, Cherry Studio, Qoder): Mediates between the user and the server.

LLM (e.g., DeepSeek): Decides which tool to use.

MCP Server: Hosts a toolbox of MCP tools (e.g., web search, file read, database query) that perform the actual work.

Execution Process

Stage 1 – Demand & Analysis (Steps 1‑3)

Input: User asks a question that requires external data, e.g., “What’s the weather today?”

Select MCP tool: The LLM recognises it lacks the information and decides to use a weather‑query tool.

Return MCP tool: The LLM tells the MCP client, “I want to use the ‘weather query’ tool.”

Stage 2 – Security Approval (Step 4)

MCP tool approval: The system asks the user for permission to let the AI call the selected tool. The workflow proceeds only after the user clicks “Agree.”

Stage 3 – Execution & Feedback (Steps 5‑8)

Request tool call: After approval, the client sends a request to the MCP server.

Invoke MCP tool: The server runs the chosen tool (e.g., fetches weather data).

Tool output: The tool returns its result, such as “Beijing is sunny, 25 °C.”

Return output: The server packages the result and sends it back to the client.

Stage 4 – Reporting (Steps 9‑10)

Send output and query to LLM: The client forwards both the original question and the tool’s result to the LLM.

Generate answer: The LLM composes a natural‑language response, e.g., “Today’s weather in Beijing is sunny with a temperature of 25 °C.”

Theory Meets Practice

Demo platform: Cherry Studio (https://www.cherry-ai.com/).

Configuration steps:

Enable MCP in the settings and paste a ModelScope key.

Activate desired MCP services, such as the 12306 ticket‑query server ( https://www.modelscope.cn/mcp/servers/@Joooook/12306-mcp) and a time‑service server (

https://www.modelscope.cn/mcp/servers/@modelcontextprotocol/time

).

In Cherry Studio’s UI, add the server, enable it, and select the appropriate MCP when chatting.

Example request payload sent by Cherry Studio:

{
  "model": "google/gemini-3-pro-preview",
  "temperature": 1,
  "messages": [
    {
      "role": "system",
      "content": "In this environment you have access to a set of tools you can use to answer the user's question..."
    }
  ]
}

Concrete tool definitions exposed by the 12306 MCP server (XML‑style):

<tools>
  <tool>
    <name>tool-12306_M-get_current_date_06mcp</name>
    <description>获取当前日期，以上海时区（Asia/Shanghai, UTC+8）为准，返回格式为 "yyyy-MM-dd"。</description>
    <arguments>{"jsonSchema":{"type":"object","properties":{},"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}}</arguments>
  </tool>
  <tool>
    <name>tool-12306_M-get_stations_code_in_city_06mcp</name>
    <description>通过中文城市名查询该城市所有火车站的名称及其对应的 station_code。</description>
    <arguments>{"jsonSchema":{"type":"object","properties":{"city":{"type":"string","description":"中文城市名称"}},"required":["city"],"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}}</arguments>
  </tool>
  ... (other tool definitions omitted for brevity) ...
</tools>

Tool‑use rules (excerpt):

Always provide correct arguments; never pass variable names.

Call a tool only when needed; otherwise answer directly.

Avoid repeating a tool call with identical parameters.

Use the XML tag format shown in examples; other formats are invalid.

Current MCP Challenges

When many MCP tools are enabled, sending all their metadata to the LLM consumes a large number of tokens, and a proliferation of similar‑looking tools increases the risk of selecting the wrong one.

Most existing MCP implementations expose only the “tool” component of the protocol; they lack the “resource” and “prompt” components that would make the tools more AI‑friendly. Many wrappers simply re‑expose traditional web APIs without optimisation for LLM consumption.