What Is MCP (Model Context Protocol)? Definition + Examples
MCP is an open standard from Anthropic that lets AI assistants call external tools and data through a common interface. How it works, plus creative-tool examples like the 8frame server.
MCP (Model Context Protocol) is an open standard that lets AI assistants connect to external tools and data sources through one common interface, so a model can take actions in the real world instead of only generating text. Anthropic introduced and open-sourced it in November 2024, and it has since been adopted across the industry as the default way to give AI systems tools.
The problem MCP solves is combinatorial. Before it, every AI application had to build a custom integration for every tool it wanted to use: one for your files, one for your database, one for an image generator, one for each API. That's an M-times-N problem. MCP turns it into M-plus-N: a tool exposes itself once as an MCP server, any MCP-capable AI app acts as a client, and they speak the same protocol. Build the integration once, and it works everywhere the standard is supported.
That standardization is why adoption moved fast. OpenAI added MCP support across its products in early 2025, and Google confirmed support in Gemini shortly after, describing it as an open standard for the agentic era. When the major model providers all speak the same protocol, building an MCP server means your tool works with assistants from every one of them.
How MCP works
MCP has three roles. The host is the AI application the user interacts with (a chat app, a coding editor, an agent). Inside it, a client manages a connection to a server, which is the program that exposes a specific capability: your calendar, a code repository, a video generator. One host can connect to many servers at once.
Servers expose their capabilities in a few standard shapes. Tools are actions the model can call (generate an image, run a query, send a message). Resources are data the model can read (files, records). Prompts are reusable templates the server offers. The model doesn't need to know how any of it is implemented; it sees a described tool and calls it, and the server does the work and returns a result.
The flow in practice: you ask the assistant to do something, the model decides a tool call is needed, the client sends that call to the right server, the server executes and returns a result, and the model uses the result to continue. Because the protocol is standard, the same server you connect to Claude works with any other MCP-capable host without modification. That's the whole value: write the connector once.
When you use MCP
You use MCP whenever you want an AI system to act rather than just answer. A plain model call generates text. An MCP-connected model can read your data and take actions through real tools. Common situations:
- Connecting an assistant to your systems. Give a chat assistant access to your files, database, or internal tools without building a bespoke integration for each.
- Building agents. An AI agent needs tools to do anything beyond talk. MCP is the standard way to hand it those tools, so you're not maintaining a separate client per capability.
- Adding a capability to many hosts at once. If you build a tool and want it usable from Claude, an IDE, and a custom agent, exposing it as one MCP server covers all three.
The signal that you want MCP is repetition: if you'd otherwise write the same kind of integration more than once, or want one tool to work across multiple AI apps, the standard is doing exactly the job it was designed for.
Examples
A creative generation server (8frame). 8frame runs a live MCP server at https://mcp.8frame.co/mcp that exposes image generation, image editing, upscaling, and video generation across 16 AI models. Once you connect it, an assistant like Claude can generate a product still, animate it, and upscale the result, all through tool calls, without you opening the app. You add it in claude.ai under Settings → Connectors → Add custom integration (which requires Claude Pro, Team, or Enterprise), or in an MCP-capable editor like Cursor or Claude Code by adding the server URL to the client config. The model describes a shot; the server routes it to the right engine and returns the clip. Cost is per-generation compute, the same per-clip pricing as generating on the canvas directly.
A code repository server. A developer tool can expose a GitHub-style server with tools like "read file," "open pull request," and "search issues." Connected to a coding assistant, the model can read the codebase and take actions on the repo through those tools. The same server, built once, works in any MCP host the developer uses. This is the everyday pattern: a system that already has an API wraps it as an MCP server, and every MCP-capable assistant gains access at once.
Both examples share the same structure: a server exposing named tools, a host with a client that connects to it, and a model that calls the tools to get real work done. Whether the capability is generating video or editing code, the protocol underneath is identical.
Related concepts
An AI agent is the most common consumer of MCP. Agents need tools to act on the world, and MCP is the standard interface that supplies them, which is why the two concepts are usually discussed together.
An AI workflow is the other side of the coin: where MCP standardizes how a model reaches a single tool, a workflow chains multiple generation steps into a repeatable pipeline. On a canvas you build the workflow; through MCP you can trigger that same generation power from a chat or an agent.
Want to see MCP in action? Connect the 8frame MCP server to Claude and generate video from a conversation, or explore the full canvas and workflows it plugs into.