What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open standard that lets AI models and agents connect to external tools and data sources through a consistent interface, so the same integration works across different AI clients.
The Model Context Protocol (MCP) is an open standard for connecting AI models to the outside world. It defines a common way for an AI client (such as an assistant or agent) to discover and use external capabilities: reading data, calling tools, and taking actions, without a custom integration for every pairing.
Think of MCP as a universal adapter. Before it, each AI app needed bespoke code to talk to each tool. With MCP, a tool exposes an MCP server, and any MCP-compatible client can use it.
Why it matters
AI agents are only as useful as the systems they can reach. MCP standardizes that reach. For developers, it means building one MCP server instead of one integration per AI platform. For users, it means an assistant can plug into many tools through a shared protocol. As agentic AI grows, a common protocol reduces fragmentation the same way HTTP standardized the web.
How it works
MCP follows a client-server model:
- An MCP server wraps a tool or data source and advertises what it can do, such as available tools, resources, and prompts.
- An MCP client, embedded in an AI app, connects to the server, discovers those capabilities, and calls them on the model's behalf.
- The model uses the results as context to reason and act.
Because the interface is standardized, a single server works across compatible clients, and a single client can talk to many servers.
MCP for video
Media tools can expose their capabilities over MCP so agents can process video programmatically. Vidocu provides an MCP server (and API) that lets AI agents upload a video and generate subtitles, documentation, voiceover, and translations as callable tools, bringing video processing into agentic workflows.
Why it matters
An open integration standard
MCP defines a consistent way for AI models and agents to connect to external tools and data sources.
Build once, use everywhere
A tool exposes one MCP server, and any MCP-compatible AI client can use it, avoiding custom integrations per platform.
Client-server model
MCP servers advertise tools and resources, MCP clients discover and call them on the model's behalf.
Built for agentic AI
As AI agents take more actions, a shared protocol reduces fragmentation across the ecosystem.
Applies to media too
Video tools can expose subtitles, documentation, and voiceover as MCP tools so agents can process video programmatically.
Examples
- •An AI assistant reading from a database through that database's MCP server.
- •An agent creating tickets in a project tool exposed over MCP.
- •A coding assistant accessing a repository's files via an MCP server.
- •An agent uploading a video and generating subtitles and documentation through a video MCP server.
Frequently asked questions
It is used to connect AI models and agents to external tools and data through a standard interface, so an AI client can read data and call tools without a custom integration for each one.
An MCP server wraps a tool or data source and advertises its capabilities, such as available tools and resources, so any MCP-compatible AI client can discover and use them.
An API is a specific interface for one service. MCP is a standard layer on top, so AI clients can discover and use many services in a consistent way. A tool can offer both an API and an MCP server.
Agents are only useful if they can reach real systems. MCP standardizes that connection, so developers build one server instead of one integration per AI platform, and agents can plug into many tools.
Yes. A video tool can expose actions like generating subtitles, documentation, and voiceover as MCP tools, letting AI agents process video as part of a larger workflow.
Vidocu offers an MCP server and API so AI agents can upload a video and generate subtitles, documentation, voiceover, and translations as callable tools, in 65+ languages.
Related terms
Learn more
- Vidocu API — Process video programmatically with subtitles, docs, and voiceover.
- Developers — Build video processing into your AI agents and apps.
- Video MCP servers (guide) — How AI agents can process videos over MCP.
- API Documentation — Reference for the Vidocu API and MCP tools.