This repository contains complete developer documentation for building, running, and integrating Model Context Protocol (MCP) Servers — a powerful way to connect AI models (like Claude or GPT) with real-time data, tools, and business logic.
🔍 What is MCP?
Model Context Protocol (MCP) is an open standard that allows large language models (LLMs) to request and consume real-time context from external systems securely via defined capabilities. MCP Servers act as the backend interface that exposes those capabilities.
Think of it as an API bridge between your AI model and your systems — clean, secure, and AI-friendly.
📂 Project Structure
This documentation is structured as a GitBook and organized into the following sections:
📘 mcp-server-docs/
├── introduction/ # What is MCP, why use MCP Servers
├── architecture/ # Core components and data flow
├── getting-started/ # Setup, prerequisites, and SDK usage
├── use-cases/ # Real-world applications
├── advanced-topics/ # Capabilities, security, logging
├── integration/ # Internal systems and LLM integration
├── testing-debugging/ # Tools and testing strategies
├── resources/ # Official docs, tools, SDK links
├── faq/ # Frequently asked questions
└── changelog.md # Project release log
🚀 Getting Started
🔧 Clone the Repo
git clone https://github.com/your-org/mcp-server-docs.git
cd mcp-server-docs
📘 Open in GitBook
1.Import this repo into GitBook 2.GitBook will auto-generate navigation from SUMMARY.md 3.Share the live docs with your team or make it public
💡 Who Is This For?
This documentation is for:
- Developers building custom AI integrations
- Teams deploying secure MCP Servers
- Architects designing LLM + enterprise system workflows
📚 Helpful Links
🧠 Contributing
We welcome internal contributions! If you’ve built a new capability, integration, or best practice, feel free to open a PR and update the relevant doc pages.
🔄 Versioning
See Changelog for release history and upcoming improvements.
💬 For questions or suggestions, reach out to the AI Platform or DevOps team.
