Model Context Protocol (MCP)
Model Context Protocol (MCP) is an open standard introduced by Anthropic in 2024 that defines how AI models connect to external tools and data sources through a unified interface. Before MCP, every integration between a model and an external system—a database, a file system, an API—required custom glue code. MCP standardizes this with a client-server architecture: the AI application is the client, and each data source or tool runs an MCP server that exposes its capabilities in a format any MCP-compatible model can understand. Think of it as USB for AI integrations. The protocol handles tool discovery, authentication, and structured input/output so developers build the connector once and any MCP client can use it. MCP matters because the value of AI systems increasingly depends on what they can access and act on, not just what they know from training.
Related terms:
LLM (Large Language Model)
A large language model is a neural network with billions of parameters trained on massive text corpora to predict the next word in a sequence, powering tasks...
Token
In large language models, a token is the basic unit of text—usually chunks of three to four characters—that the model reads and generates.
Inference
Inference is the process of running a trained model on new input to generate a prediction or output—such as sending a prompt to GPT-4 and receiving a...