MCP(Model Context Protocol): The Universal Protocol Bridging AI with the Real World

1. Why Is MCP (Model Context Protocol) Blowing Up?

AI has entered the "integration era." Even the most powerful model can't do much if it's stuck in a static chat box.

That’s where MCP(Model Context Protocol) comes in — a protocol that gives AI models the ability to access tools, services, and real-time data.

Think of MCP as the USB of AI — it gives LLMs the "hands and feet" to browse the web, use apps, read/write databases, and complete real-world tasks.

2. What Is Model Context Protocol? (Simple vs Technical)

✅ Engineering Definition

MCP (Model Context Protocol) is an open protocol (backed by companies like Anthropic ) that standardizes how LLMs communicate with external tools. It enables dynamic tool calling, real-time data access, and multi-step execution.

📌 Technical Features:

• Defines the communication flow between the Client and Server
• Supports streaming responses, multi-step tool calls, and context-aware permissions
• Functions as a superset of traditional AI Function Calling, making it ideal for building full-fledged AI agents

✅ Human Analogy

MCP is like a universal adapter and control protocol for AI.

Imagine:

• LLM = Operating System
• MCP = USB/Bluetooth
• External Tools = Headphones, Printer, Scanner

Without MCP, the model can only “read books.” With MCP, it can interact with the world.

3. Why Is MCP Protocol Better Than Traditional APIs?

Many people ask: “If I can already connect tools using APIs, why do I need MCP?”

Here’s the answer:
With APIs, you write the code. With MCP, the model writes the workflow.

Simply put: Traditional APIs are made for humans. MCP is made for AI.

Image source: norahsakal blog

4. Model Context Protocol Server Ecosystem on GitHub

The MCP ecosystem is exploding. The awesome-mcp-servers repo has 30K+ stars and 3000+ MCP Servers implementations across over 20 domains like:

📌 Here are some examples of MCP Servers from the open-source ecosystem:

Note: Many of these servers support multiple models — Claude, GPT-4, Gemini, etc.

5. Real-World Examples of Model Context Protocol: Booking a Flight

📌 Imagine you tell the model:

“Book me a flight to Paris tomorrow and add it to my Notion calendar.”

Without MCP, this command cannot be accomplished:

• The model doesn't know flight data

• It cannot access APIs like Ctrip

• It cannot write data to your Notion

With MCP, all of this becomes possible:

sequenceDiagram
  participant User
  participant LLM
  participant MCP_Server
  participant BookingAPI
  participant NotionAPI

  User->>LLM: Please book a flight and sync the calendar
  LLM->>MCP_Server: Request to search flights
  MCP_Server->>BookingAPI: Retrieve Paris flights
  BookingAPI-->>MCP_Server: Return flight list
  MCP_Server-->>LLM: Provide flight options
  LLM->>MCP_Server: Call booking + Notion calendar API
  MCP_Server->>NotionAPI: Write schedule

👉 What you see is the "result," while the AI model accomplishes MCP multi-tool collaboration in the background!

6. The Three-tier Architecture of MCP Protocol: Host, Client, Server

Here is the standard communication architecture of the MCP protocol, which includes three core roles:

📊 Mermaid Architecture Diagram:

graph TD;
    HOST[LLM Frontend] --> CLIENT[MCP Client]
    CLIENT --> S1[Browser Server]
    CLIENT --> S2[PDF Server]
    CLIENT --> S3[Database Server]

📌 Explanation:

• Host is the "command issuer," such as the Claude chat interface.

• Client acts as the "translator and dispatcher," directing requests to different Servers based on the task.

• Server is the "actual worker," executing tasks and returning structured results.

7. How Does an MCP Server Work? (Developer's Perspective)

An MCP Server is the smallest working unit in the MCP ecosystem, with each Server being a "tool controllable by AI."

✅ Core Responsibilities of a Server:

1. Expose a standardized JSON interface (OpenAPI format).
2. Declare its capabilities (metadata).
3. Receive standard requests from the MCP Client, execute operations, and return responses.

📦 Example Server File Structure (using ⁠mcp-browser as an example):

📁 mcp-browser/

├── openapi.yaml       # Function definition file
├── main.py            # FastAPI startup logic
├── actions.py         # Executes specific operations, such as browser control
├── metadata.json      # MCP Server metadata definition
└── requirements.txt   # Required dependencies

🔧 Example Code Snippet (developing a simple "calculator" Server using FastAPI):

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Operation(BaseModel):
    a: float
    b: float
    op: str

@app.post("/calculate")
def calculate(op: Operation):
    if op.op == "add":
        return {"result": op.a + op.b}
    if op.op == "sub":
        return {"result": op.a - op.b}
    return {"error": "Unsupported"}

Also, configure a ⁠metadata.json:

{
  "name": "calculator-mcp-server",
  "description": "Basic arithmetic operations",
  "url": "http://localhost:8000",
  "openapi_spec": "/openapi.json"
}

📌 This completes a tool that can be called by an AI model through MCP.

8. How to Register and Connect Multiple Model Context Protocol Servers?

Each MCP Client can configure multiple MCP Servers, and all Servers are registered in a ⁠registry (which can be a local JSON file or a remote configuration center).

✅ Typical Registration Structure::

[
  {
    "name": "notion",
    "description": "Notion content writing",
    "url": "http://localhost:3001",
    "openapi": "/openapi.json"
  },
  {
    "name": "search",
    "description": "Invoke search engine",
    "url": "http://localhost:3002",
    "openapi": "/openapi.json"
  }
]

📌 Agent systems like Claude, LangChain, and AutoGPT will use the MCP Client to retrieve available Servers from this registry and decide which tool to invoke based on the conversation context.

9. How MCP Works with RAG and Agents：From "Prompt Stacking" to "Chained Intelligent Systems"

MCP doesn't exist in isolation; it's naturally an extension of the Agent architecture, while RAG is its data input source. Together, they form a powerful "AI multitasking chain."

📊 Collaboration Diagram:

flowchart TD

    U[User Command] --> AG[Agent System]

    AG --> RAG[Retrieve Data with RAG]

    AG --> MCP[Invoke MCP Tool Server]

    RAG --> CONTEXT[Provide Enhanced Knowledge Context]

    MCP --> RESULT[Return Execution Result]

    CONTEXT --> AG

    RESULT --> AG

    AG --> RESPONSE[Final Response Output]

✅ Practical Example: PDF + Search + Summary

"Help me extract the content of this PDF, supplement it with background information you can find, and generate a summary."

• Agent: Breaks down tasks (extract, retrieve, generate)

• RAG: Connects to company knowledge bases + Wikipedia API

• MCP Server:

• ⁠pdf-reader: Parses PDF documents

• ⁠search: Searches for relevant background

• ⁠summarizer: Compiles into a summary

📌 The LLM only handles the calls, not the operations, which are executed by the Server!

✅ The Role of MCP:

• 🎯 Standardizing Tool Invocation Interfaces

• 🧠 Empowering Agents with Actionability

• 🔄 Integrating with RAG for Dynamic Context Building

Below is the third part (3/3) of a standard blog, focusing on the trends in the MCP tool ecosystem, enterprise applications, comparisons with Function Calling/plugin systems, and future challenges and directions.

10. Tool Ecosystem Boom: Is MCP the New "Plugin System"?

As shown by awesome-mcp-servers, MCP has established an initial developer ecosystem and is gradually replacing traditional plugins and Function Calling as the mainstream AI tool integration method.

✅ Current Capabilities Covered by MCP Servers Include:

• 🧭 Browser control (based on Playwright)

• 📄 PDF/EPUB/Word document parsing

• 🔎 Search engine invocation (DuckDuckGo, Brave, Bing API)

• 🧠 Semantic search (vector databases)

• 📅 Calendar, Notion, databases (MySQL, MongoDB)

• 🔍 Arxiv/PubMed/Hacker News search

• 📈 Data analysis/chart automation

Any AI engineer can register their tool into the intelligent system by creating an MCP Server that complies with ⁠openapi.json and ⁠metadata.json.

📌 Trend Comparison:

👉 MCP is more like the "standard bus" for future LLM tool ecosystems, allowing for horizontal expansion, automatic registration, and compatibility with any LLM.

11.  How Can Enterprises Build an MCP Toolchain?

🏢 Application Scenario 1: Intelligent Customer Service System

Goal: Achieve a trinity of "self-service Q&A + ticket submission + external system operations"

📌 User Experience: The customer service AI can both query documents for answers and create tickets, updating the database.

💼 Application Scenario 2: Financial/Legal AI Assistant

Goal: Review contracts + compare policies + generate audit reports

• Use RAG to extract regulations

• Agent controls process execution

• MCP Server executes:

• Structured PDF document extraction

• Key clause extraction and comparison

• Report writing and automatic archiving

📌 Complete audit tasks with one click, saving 80% of labor costs.

✅ Final Thoughts: Will MCP(Model Context Protocol) Become the Infrastructure of the AI Application Ecosystem?

📌 Combining observations from the official website mcp.so and developer communities, MCP (Model Context Protocol) is likely to evolve in the following directions:

1. Support for Multimodal Model Invocation: Beyond text, future capabilities may include image recognition, video manipulation, etc.
2. MCP Hub Platformization: A marketplace for MCP Server registration and discovery, similar to a "Plugin Store"
3. Integration with RAG and Agent Standards: Seamless integration with frameworks like LangChain / AutoGen / LangGraph
4. Cross-Platform Adaptation: Unified invocation of external systems across major models like GPT-4 / Claude / Gemini / DeepSeek

Returning to the initial question—why is everyone talking about MCP?

The significance of MCP goes beyond being a "new protocol"; it represents a pivotal step in the evolution of AI systems from "closed language models" to "open intelligent agents":

• ✨ In the past, large models were like "Turing machines," working in isolation

• ⚙️ Now, with RAG, they can access external knowledge

• 🧠 With Agents, they can think and plan tasks

• 🔌 With MCP, they can truly "get things done"!

These three components form a closed-loop AI workflow with perception, cognition, and action capabilities.

📌 MCP is the link connecting to the real world, enabling AI to step out of the screen, integrate into systems, and truly "get things done."

📎 Recommended Reading Resources

• awesome-mcp-servers: MCP Server tool index

• Norah Sakal's Blog: In-depth analysis of MCP vs API

• MCP Official Website

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