MCP (Model Context Protocol)
A protocol that allows AI models to connect to external tools, APIs, and data sources in real time. Enables AI assistants to take actions like searching databases, running code, or calling APIs during a conversation.
Why it matters
AI models are powerful but isolated; on their own they can't check your calendar, read a database, or use live tools. MCP is a shared standard that plugs models into those outside resources safely and consistently. It matters because it turns a chatbot into an assistant that can actually do things with your real data. As more tools support it, connecting AI to your workflow gets far simpler.
In practice
You ask an AI assistant, 'What's on my schedule tomorrow, and email a summary to my team?' Through MCP, the model reaches into your calendar tool, pulls the real appointments, then connects to your email tool to send the note. Instead of you copying details between apps, the assistant uses these live connections to complete the whole task. MCP is the common adapter that makes those tool hookups work reliably.
Related terms
AI Dubbing
Automated translation and re-voicing of audio/video content into other languages while preserving the original speaker's voice characteristics, timing, and emotional delivery.
AI Orchestration
Coordinating multiple AI models, tools, and data sources in a unified pipeline. An orchestration layer manages prompt routing, context passing, error handling, and output aggregation across different AI services.
API (Application Programming Interface)
A way for software applications to communicate with each other. AI APIs let developers integrate AI capabilities into their own applications programmatically.
Attention Mechanism
A technique that allows AI models to focus on the most relevant parts of input data when generating output. In language models, attention determines which words in a sentence are most important for understanding each other word.
Diffusion Model
A type of generative AI that creates images by gradually removing noise from a random starting point. Powers tools like Stable Diffusion, DALL-E, and Midjourney. Works by learning the reverse of a noise-adding process.
Distillation
A technique where a smaller 'student' model learns to replicate the behavior of a larger 'teacher' model. Produces compact models that retain most of the teacher's capability while being faster, cheaper, and deployable on smaller devices.