Model Context Protocol (MCP)
Model Context Protocol (MCP) is an open specification that gives AI assistants and agents one shared way to plug into outside tools, data and services. An MCP server exposes actions, such as uploading a video or fetching playback analytics, that any compatible AI client can discover and call. MCP works like a universal adapter, so one integration serves many AI applications.
Why it matters
AI assistants are becoming a common way people use software. MCP lets a product expose its actions once and have them work across many AI clients, instead of building a custom integration for each. For video platforms, that means agents can upload, search or analyze video on a user's behalf.
How it works
- A service publishes an MCP server that lists its tools, such as upload video or get analytics.
- An AI client, such as a chat assistant, connects to the server and reads that list.
- When a user asks for something, the model picks the right tool and sends a structured request.
- The server performs the action and returns the result, which the model uses in its reply.
What is the difference between MCP and an API?
An API is the interface a service offers to programs. MCP is a standard layer on top that describes those capabilities in a way AI models can discover and call. An MCP server usually calls the service's existing API behind the scenes. The difference is the audience: APIs are written for developers, MCP for AI agents.
MCP vs. llms.txt: what is the difference?
llms.txt is a plain text file on a website that points AI tools to the most useful documentation, so they can read about a product. MCP lets an AI agent take actions, such as creating or querying resources. In short, llms.txt helps an assistant learn about a service, while MCP lets it use the service.
Where it's used
SaaS products and generative AI video companies use MCP to let AI assistants take real actions, such as finding a video, adding captions or checking playback stats, without custom code for each assistant.
Where it usually goes wrong, and how FastPix handles it
Teams often expose an API to AI agents without machine-readable documentation, so assistants guess at endpoints and parameters and produce broken calls.
FastPix publishes an llms.txt so AI assistants can read its documentation. An MCP server is not available yet, so agents work with the FastPix REST API directly. That gives coding assistants accurate endpoints to work from, so your developers spend less time fixing guessed calls.
By the numbers
- Anthropic introduced the Model Context Protocol on November 25, 2024. (Model Context Protocol (Wikipedia))
- OpenAI officially adopted MCP in March 2025, including in the ChatGPT desktop app. (Model Context Protocol (Wikipedia))
- In December 2025, Anthropic donated MCP to the Agentic AI Foundation, a directed fund under the Linux Foundation. (Model Context Protocol (Wikipedia))
