If you've been following AI development in 2025, you've probably heard of MCP—the Model Context Protocol. It's being adopted by Anthropic, OpenAI, Google, and dozens of other companies. But what exactly is it, and why does it matter?
In this guide, we'll explain MCP from the ground up: what it is, how it works, and why it's becoming the standard way to connect AI models to external tools and data.
MCP in Simple Terms
Think of MCP as a universal adapter for AI. Just like USB-C lets you connect any device to any port with a single standard cable, MCP lets any AI model connect to any external service through a single standard protocol.
Before MCP, if you wanted Claude to access your database, you'd build a custom integration. If you wanted ChatGPT to access the same database, you'd build a different custom integration. Each combination of AI + service required its own connector.
With MCP, you build one server that exposes your database, and any MCP-compatible AI client can use it—Claude, ChatGPT, Cursor, or any other tool that supports the protocol.
The Model Context Protocol is an open standard that enables developers to build secure, two-way connections between their data sources and AI-powered tools. It standardizes how applications provide context to large language models (LLMs).
The Problem MCP Solves
Large language models like GPT-4 and Claude are incredibly capable, but they have a fundamental limitation: they're isolated from the real world.
An LLM on its own can only:
- Generate text based on its training data
- Answer questions about information it was trained on
- Follow instructions within the conversation
What it cannot do without external help:
- Access current information (news, stock prices, weather)
- Read your files or databases
- Send emails or messages
- Create or modify documents
- Interact with APIs or web services
The N×M problem
Before MCP, connecting AI to external services required custom integrations. If you have N AI models and M services, you potentially need N×M custom integrations. This doesn't scale.
Every developer building AI tools was reinventing the wheel—writing the same database connectors, the same API wrappers, the same authentication flows.
MCP's solution
MCP flips this equation. Instead of N×M integrations, you need:
- Each AI client implements MCP once (N implementations)
- Each service exposes an MCP server once (M implementations)
- Total: N + M implementations instead of N × M
If Slack publishes an MCP server, every MCP-compatible AI client can use it automatically. No custom integration needed.
MCP Architecture
MCP uses a client-server architecture with three main components:
1. MCP Host
The host is the AI application that users interact with—Claude Desktop, ChatGPT, Cursor, or a custom app. The host contains the LLM and coordinates with MCP clients to access external capabilities.
2. MCP Client
The client lives inside the host and handles communication with MCP servers. It discovers available tools, translates requests into the MCP format, and returns results to the LLM. Each client connects to one server (1:1 relationship).
3. MCP Server
The server is what developers build. It exposes tools, resources, and prompts that AI clients can use. Servers connect to your data sources—databases, APIs, file systems—and make them accessible through the standardized protocol.
Communication flow
User Query
↓
[MCP Host] (Claude Desktop, ChatGPT, etc.)
↓
[MCP Client] — translates request to MCP format
↓
[MCP Server] — your custom server
↓
[Data Source] (database, API, file system)
↓
Response flows back up the chainThe protocol uses JSON-RPC 2.0 for message formatting, providing a structured way to handle requests, responses, and errors.
Core Components: Tools, Resources, Prompts
MCP servers can expose three types of capabilities:
Tools
Tools are functions that perform actions. They're similar to function calling in OpenAI's API, but standardized across all MCP clients.
Each tool has:
- Name: Identifier for the tool (e.g.,
send_email) - Description: What the tool does (used by the AI to decide when to call it)
- Input schema: JSON Schema defining accepted parameters
- Handler: Function that executes when the tool is called
Example tools: create_task, query_database, send_message, get_weather
Resources
Resources provide read-only data access. Unlike tools, they don't perform actions—they just return information.
Resources use URI schemes to identify data:
file:///path/to/document.txt— Local filespostgres://database/users— Database tablesweather://cities— Custom data sources
Resources can be static (fixed URI) or templated (dynamic URIs with parameters).
Prompts
Prompts are pre-defined templates that help users and AI models interact with your server effectively. They're optional but can significantly improve user experience.
Example: A "weekly report" prompt template that guides the AI to use specific tools in a specific order to generate a report.
Transport Protocols
MCP is transport-agnostic, meaning the same server logic works over different communication channels:
stdio (Standard Input/Output)
Used for local servers. The host spawns the MCP server as a subprocess and communicates via stdin/stdout. This is how Claude Desktop connects to local MCP servers.
Best for: Desktop applications, local development, CLI tools
HTTP (Streamable HTTP)
Used for remote servers. Communication happens over HTTP/HTTPS with support for streaming responses. This is how ChatGPT connects to MCP servers.
Best for: Web applications, remote services, production deployments
SSE (Server-Sent Events)
An older HTTP-based transport that uses separate endpoints for sending and receiving. Still supported but Streamable HTTP is now preferred.
MCP vs Other Approaches
| Approach | Description | Limitations |
|---|---|---|
| Function Calling | Model-specific API for calling functions | Vendor-specific, no UI support, manual integration |
| ChatGPT Plugins | OpenAI's earlier plugin system | Proprietary, limited to ChatGPT, deprecated |
| RAG | Retrieval-Augmented Generation | Read-only, no actions, requires vector DB |
| Custom APIs | Build your own integration | N×M problem, no standardization |
| MCP | Open standard protocol | Requires adoption, learning curve |
MCP doesn't replace function calling—it builds on top of it. The protocol standardizes how tools are defined, discovered, and invoked across different AI platforms.
Build MCP apps without code
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Join the waitlistWho's Using MCP?
MCP has seen rapid adoption since its release. Here's a timeline of major milestones:
MCP clients (AI applications)
- Claude Desktop — Anthropic's desktop app
- ChatGPT — Via the Apps SDK
- Cursor — AI-powered code editor
- Windsurf — AI coding assistant
- Cline — VS Code extension
- Zed — Code editor with AI features
Popular MCP servers
- GitHub — Repository access and management
- Slack — Messaging integration
- Google Drive — File access
- PostgreSQL — Database queries
- Puppeteer — Browser automation
- Figma — Design file access
Getting Started with MCP
Ready to build with MCP? Here are your options:
As a developer (building servers)
- Choose your SDK: TypeScript or Python
- Define your tools, resources, and prompts
- Implement handlers for each capability
- Test with the MCP Inspector
- Connect to Claude Desktop or deploy for ChatGPT
See our MCP Server Tutorial for step-by-step instructions.
As a user (using existing servers)
- Install Claude Desktop or use ChatGPT
- Find MCP servers in the official repository
- Configure the server in your client's settings
- Start using the new capabilities in conversations
Without code
If you don't want to write code, platforms like Agentappbuilder let you create MCP-compatible apps through visual interfaces. You design your tools and data connections, and the platform generates the MCP server for you.
The Future of MCP
MCP is still evolving. Here are some developments on the horizon:
Enhanced security
The protocol is adding more robust authentication mechanisms, including OAuth 2.1 support and finer-grained permission controls.
Broader adoption
As more AI platforms adopt MCP, the ecosystem of available servers will grow. This creates a network effect—more servers mean more value for clients, which drives more client adoption.
Standardization
With the protocol now under the Linux Foundation's Agentic AI Foundation, expect more formal standardization and governance processes.
Specialized extensions
Domain-specific extensions (like the OpenAI Apps SDK for ChatGPT) will continue to add platform-specific features while maintaining protocol compatibility.