📘 MCP Server Documentation

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.

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🔍 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.

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📂 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

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🚀 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

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💡 Who Is This For?

This documentation is for:

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

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📚 Helpful Links

- ModelContextProtocol.io
- Anthropic Claude + Tools
- OpenAI Function Calling
- GitHub SDK Repos

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🧠 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.

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🔄 Versioning

See Changelog for release history and upcoming improvements.

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> 💬 For questions or suggestions, reach out to the AI Platform or DevOps team.