MCP Server Gitbook

This project is a developer documentation about the Model Context Protocol (MCP) Server, which details how to build, run, and integrate the MCP Server to achieve a secure connection between AI models and real-time data, tools, and business logic.

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update time : 2025-04-28

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What is the MCP Server?

The Model Context Protocol (MCP) Server is an open-standard backend interface that allows large language models (such as Claude or GPT) to securely request and consume real-time context information from external systems through defined capabilities. It acts as an API bridge between AI models and your systems.

How to use the MCP Server?

Using the MCP Server requires three basic steps: 1) Deploy an MCP server instance. 2) Configure the required capabilities. 3) Connect the AI model to the server through the standard protocol.

Use cases

The MCP Server is particularly suitable for scenarios that require integrating AI models with enterprise systems, real-time data, or professional tools, such as customer service automation, data analysis enhancement, and business process optimization.

Main features

Real-time context access

Allows AI models to query and obtain the latest information in external systems in real-time

Secure integration

Ensures the security of data exchange through a standardized protocol and permission control

Capability management

Flexibly defines and expands the set of capabilities exposed by the server to AI models

Multi-model support

Is compatible with mainstream AI models such as Claude and GPT

Advantages

Provides a standardized interface between AI models and enterprise systems

Supports real-time data access, enhancing the accuracy of AI decision-making

Flexible permission and capability control mechanisms

Reduces the problem of AI hallucination

Limitations

Requires additional server deployment and maintenance

May require adaptation work for the integration of existing systems

Performance depends on the response speed of external systems

How to use

Clone the documentation repository

First, obtain the documentation and configuration examples of the MCP Server

Deploy the server

Select an appropriate deployment method (container, cloud service, or local) according to your environment

Configure capabilities

Define the set of capabilities and permissions that the server will expose to AI models

Connect the AI model

Configure your AI model to communicate with the server using the MCP protocol

Usage examples

Customer service automation

AI customer service agents access customer order and account information in real-time through the MCP Server to provide accurate customer support

Real-time data analysis

AI analysts obtain the latest sales data through the MCP and generate insight reports

Frequently Asked Questions

What is the difference between the MCP Server and a regular API?

What technology stack is required to deploy the MCP Server?

How to ensure the security of the MCP Server?

Related resources

Model Context Protocol official website

Official protocol standards and specification documents

GitHub SDK repository

SDKs and sample code in various languages

Claude tool documentation

How to integrate MCP with Claude AI

OpenAI function calling

Guide for GPT models to use external functions

🚀 MCP Server Documentation

This repository contains comprehensive developer documentation for building, running, and integrating the Model Context Protocol (MCP) Server — a powerful approach to connect AI models (such as Claude or GPT) with real-time data, tools, and business logic.

🔍 What is MCP?

The Model Context Protocol (MCP) is an open standard that enables large language models (LLMs) to request and consume real-time context from external systems through defined functions in a secure and reliable manner. The MCP server acts as a backend interface, exposing these functions.

You can think of it as an API bridge between AI models and your system — clean, secure, and AI-friendly.

📂 Project Structure

This documentation is a GitBook organized into the following sections:

📘 mcp-server-docs/
├── introduction/          # What MCP is and why use the MCP server
├── architecture/          # Core components and data flow
├── getting-started/       # Setup, prerequisites, and SDK usage
├── use-cases/             # Real-world application scenarios
├── advanced-topics/       # Features, security, and logging
├── integration/           # Integration of internal systems and LLMs
├── testing-debugging/     # Tools and testing strategies
├── resources/             # Links to official documentation, tools, and SDKs
├── faq/                   # Frequently asked questions
└── changelog.md           # Project release log

🚀 Quick Start

🔧 Clone the Repository

git clone https://github.com/your-org/mcp-server-docs.git
cd mcp-server-docs

📘 Open in GitBook

Import this repository into GitBook.
GitBook will automatically generate navigation from SUMMARY.md.
Share the live documentation with your team or publish it publicly.

💡 Who Is This For?

This documentation is suitable for:

Developers building custom AI integrations
Teams deploying secure MCP servers
Architects designing LLM + enterprise system workflows

📚 References

🧠 Contribution Guidelines

Internal contributions are welcome! If you've built new features, integrations, or best practices, feel free to submit a PR to update the relevant documentation pages.