The Model Context Protocol (MCP) — A Complete Tutorial

Anthropic released the Model Context Protocol (MCP) in November 2024, developed by Mahesh Murag at Anthropic. MCP is now fully implemented with both Python SDK and TypeScript SDK.

Context is the Key

The basic capabilities of a Generative AI model depend on its pretraining details, the training data, and the model architecture. To make these pretrained models perform better and improve their relevance and coherence to your task, you must provide good context.

What is Context?

Context refers to the information the model uses to generate relevant and coherent responses. Context determines how the model understands and continues a conversation, completes a text, or generates an image.

Context can be provided in different ways:

Text-Based Models (GPT, DeepSeek, LLaMA)

Prompt Context: Input text that guides the model's response
Token Window: Number of tokens the model can "remember" (e.g., GPT-4-Turbo handles ~128K tokens)
Conversation History: Previous exchanges in multi-turn dialogues
Retrieval-Augmented Generation (RAG): Context from external documents retrieved dynamically

Image and Multimodal Models (DALL·E, Gemini)

Text Descriptions: Prompts that guide image generation
Visual Context: Existing images that inform new generation
Cross-Modal Context: Combined text and image interpretation

Code Generation Models (Codex, DeepSeek-Coder)

Previous Code Blocks: Existing code, function names, and comments
Programming Language Syntax: Language-specific patterns
External Documentation: APIs or docs for accurate suggestions

Speech and Audio Models (Whisper, AudioPaLM)

Audio Segments: Prior speech or music
Linguistic and Acoustic Features: Tone, speed, and intonation

The Fragmentation Problem

Before MCP, building AI systems often involved:

Custom implementations for each AI application to hook into required context
Inconsistent prompt logic and different methods for accessing tools and data
The "N times M problem" where many client applications needed to interact with many servers and tools

Each data source was implemented in its own way (open source packages, JSON RPC messages, etc.) with no standard way for AI models to search for and request data.

Model Context Protocol (MCP) Solution

MCP solves this problem of fragmented data access by providing an open standard for connecting AI systems with data sources and tools, replacing fragmented integrations with a single protocol.

MCP provides fungibility between AI clients and servers, offering a standardized way for applications to:

Share contextual information with language models
Expose tools and capabilities to AI systems
Build composable integrations and workflows

MCP Architecture

MCP follows a client-host-server architecture where each host can run multiple client instances.

The Protocol Foundation

MCP uses JSON-RPC 2.0 messages to establish communication between:

Hosts: LLM applications that initiate connections
Clients: Connectors within the host application
Servers: Services that provide context and capabilities

This architecture enables users to integrate AI capabilities across applications while maintaining clear security boundaries and isolating concerns.

Host Process

The host acts as the container and coordinator:

Creates and manages multiple client instances
Controls client connection permissions and lifecycle
Enforces security policies and consent requirements
Handles user authorization decisions
Coordinates AI/LLM integration and sampling
Manages context aggregation across clients

Client Instances

Each client is created by the host and maintains an isolated server connection:

Establishes one stateful session per server
Handles protocol negotiation and capability exchange
Routes protocol messages bidirectionally
Manages subscriptions and notifications
Maintains security boundaries between servers

A host application creates and manages multiple clients, with each client having a 1:1 relationship with a particular server.

MCP Clients

MCP Clients are the AI applications or agents that want to access external systems. Examples include:

Anthropic's first-party applications
Cursor
Windsurf
Agents like Goose

Key characteristics of MCP clients:

MCP compatibility built to communicate using standardized interfaces
Can connect to any MCP server with minimal additional work
Responsible for invoking tools, querying resources, and interpolating prompts
Language model decides when to invoke tools exposed by servers
Control over how data exposed by servers is used

MCP Servers

MCP Servers act as wrappers or intermediaries providing standardized access to various external systems. An MCP server can provide access to:

Databases
CRMs like Salesforce
Local file systems
Version control systems like Git

Server responsibilities:

Expose tools, resources, and prompts in a consumable way
Can be adopted by any MCP client (solving the "N times M problem")
Define available functions and descriptions for tools
Create or retrieve data that's exposed to client applications
Provide predefined templates for common interactions

Server Capabilities

Servers provide specialized context and capabilities:

Expose resources, tools and prompts via MCP primitives
Operate independently with focused responsibilities
Request sampling through client interfaces
Must respect security constraints
Can be local processes or remote services

MCP Primitives

MCP defines three core primitives that servers expose:

Resources

Static or dynamic data that servers provide:

File contents
Database records
API responses
Configuration data

Tools

Functions that clients can invoke:

Calculations
Database queries
File operations
External API calls

Prompts

Predefined templates for common interactions:

Code review templates
Analysis frameworks
Communication drafts

Benefits of MCP Implementation

Standardization

Common interface for integrating various tools and data sources
Reduced development time and complexity
Consistent integration patterns

Enhanced Performance

Direct access to data sources
Faster, more accurate responses from AI models
Real-time data connectivity

Flexibility

Easy switching between different LLMs
No need to rewrite code for each integration
Modular architecture

Security

Robust authentication and access control mechanisms
Secure data exchanges
Isolated server boundaries

Popular Tools Supporting MCP

MCP is gaining widespread adoption across AI tools:

Claude Desktop
Claude Code
Cursor
Windsurf (Codeium)
Cline (VS Code extension)

MCP vs RAG

While Retrieval Augmented Generation (RAG) provides LLMs with custom data for contextually relevant responses, MCP goes beyond RAG by providing:

Direct access to tools and custom data through unified APIs
Real-time data connectivity rather than just retrieval
Standardized protocol for diverse integrations
Tool invocation capabilities beyond just data retrieval

Getting Started with MCP

MCP takes inspiration from the Language Server Protocol, which standardized programming language support across development tools. Similarly, MCP standardizes AI application integrations.

To begin implementing MCP:

Choose your SDK: Python or TypeScript
Define your use case: What data sources or tools do you need?
Build or find servers: Create custom servers or use existing ones
Configure your client: Set up your AI application to use MCP
Test and iterate: Ensure proper functionality and security

The separation between clients and servers offers several benefits:

Seamless Integration: Clients connect to various servers without knowing underlying system specifics
Reusability: Server developers build integrations once, use across multiple clients
Scalability: Easy to add new capabilities without modifying existing integrations

MCP represents the future of AI integration, providing the standardized foundation needed for the next generation of AI-powered applications.

Ready to dive deeper? Check out our hands-on guides for building your first MCP server and integrating with popular AI tools.