> ## Documentation Index
> Fetch the complete documentation index at: https://docs.zopio.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

> Introduction to the Model Context Protocol (MCP)

# Model Context Protocol Overview

The Model Context Protocol (MCP) is a standardized system for defining, validating, and interacting with AI model contexts across the Zopio codebase. It provides a consistent interface for context management, enabling seamless communication between different parts of your application and AI models.

## What is MCP?

MCP is designed to solve the challenge of providing structured, validated context to AI models in a consistent way. It implements a resource-based architecture that allows you to:

1. **Define** context resources with strict schemas
2. **Validate** resources against those schemas
3. **Share** resources between different parts of your application
4. **Consume** resources in AI model interactions

## Key Concepts

### Resources

Resources are the core building blocks of MCP. Each resource:

* Has a unique identifier
* Belongs to a specific type
* Contains attributes that follow a defined schema
* Can be validated against its schema

Example resource:

```json theme={"system"}
{
  "id": "user-123",
  "type": "user",
  "attributes": {
    "name": "John Doe",
    "email": "john@example.com",
    "role": "admin"
  }
}
```

### Schemas

Schemas define the structure and validation rules for resources. MCP uses [Zod](https://zod.dev/) for schema definition and validation, providing:

* Type safety
* Runtime validation
* Detailed error messages
* TypeScript integration

### Server/Client Architecture

MCP implements a server/client architecture:

* **Servers** host resources and provide endpoints for clients to access them
* **Clients** consume resources from servers and use them in AI interactions

This architecture allows for flexible deployment options, from in-memory servers for testing to full API-based servers for production.

## When to Use MCP

MCP is particularly useful when:

* You need to provide structured context to AI models
* You want to ensure context data is validated before use
* You need to share context between different parts of your application
* You're building AI features that require consistent context formats

## Getting Started

To start using MCP in your project:

```bash theme={"system"}
# Install the package
pnpm add @repo/mcp
```

Then import the necessary components:

```typescript theme={"system"}
// Import core components
import { MCPServer, MCPClient, createResource } from '@repo/mcp';

// Import schemas as needed
import { modelSchema, apiSchema } from '@repo/mcp';
```

See the other sections in this documentation for detailed usage examples and API reference.