MCP (Model Context Protocol)
A standard for exposing tools and data to a model in a uniform, discoverable way, so integrations are not hand-rolled per model. Widely adopted in 2026, with its own security concerns.
What is MCP (Model Context Protocol)?
A standard for exposing tools and data to a model in a uniform, discoverable way, so integrations are not hand-rolled per model. Widely adopted in 2026, with its own security concerns.
MCP (Model Context Protocol) is a advanced concept that sits in the LLM and GenAI Ops area of system design. Engineers reach for it whenever they need to reason about real-world trade-offs in that space, not just for textbook correctness, but because real production systems at companies like Netflix, Amazon, and Google make these decisions every day.
If you want to go deeper than this definition, with diagrams, code, and a quiz to lock it in, work through the "LLM Agents in Production" lesson linked below. It walks through the why, the mechanism, the trade-offs, and how the giants actually use it in production.
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See also
Related glossary terms you might want to look up next.
Tool Calling
When a model emits a structured request to run a function, and an orchestrator executes it and feeds the result back. The model never runs anything itself, so the orchestrator validates every call.
AI Agent
A language model placed in a loop: it decides an action, calls a tool, reads the result, and repeats until done. The hard part is that per-step errors compound over many steps.
Prompt Injection
An attack where untrusted text, often returned by a tool, carries an instruction the model obeys, because the model cannot reliably tell instructions from data. The top security risk for agents, and unsolved by prompting alone.
RAG (Retrieval-Augmented Generation)
A pattern that retrieves relevant text from an external index and puts it in the model's prompt so the answer is grounded in current, verifiable facts rather than the model's memory.
Embedding
A list of numbers that represents the meaning of a piece of text, so that similar meanings sit close together in vector space. The same model must embed both the corpus and the query.
ANN (Approximate Nearest Neighbor)
Search that finds vectors close to a query without scanning every item, trading a small chance of missing a true neighbor for a large gain in speed. HNSW and IVF are the common indexes.