Model Context Protocol (MCP)
An open standard that defines how AI models connect to external tools, data sources, and services through a unified interface. MCP enables agents to dynamically discover and invoke capabilities without hardcoded integrations.
Model Context Protocol standardizes the way AI agents interact with the outside world. Instead of building custom integrations for every tool an agent needs, MCP provides a universal connector layer. Think of it like USB for AI: any MCP-compatible tool can plug into any MCP-compatible agent without bespoke wiring.
For growth engineering teams, MCP is transformative because it decouples agent logic from tool implementation. You can swap out your CRM connector, analytics API, or content management system without rewriting agent code. This dramatically reduces the integration tax that slows down AI adoption. Teams building internal tools or customer-facing AI features should evaluate MCP-compatible frameworks to future-proof their agent infrastructure and reduce maintenance overhead as the tool ecosystem evolves.
Related Terms
Tool Use
The ability of an AI model to invoke external functions, APIs, or services during a conversation to perform actions beyond text generation. Tool use transforms language models from passive responders into active problem solvers.
Function Calling
A model capability where the AI generates structured JSON arguments for predefined functions rather than free-form text. Function calling provides a reliable bridge between natural language understanding and programmatic execution.
Agentic Workflow
A multi-step process where an AI agent autonomously plans, executes, and iterates on tasks using tools, reasoning, and feedback loops. Agentic workflows go beyond single-turn interactions to accomplish complex goals.
ReAct Pattern
An agent architecture that interleaves Reasoning and Acting steps, where the model thinks about what to do next, takes an action, observes the result, and repeats. ReAct combines chain-of-thought reasoning with tool use in a unified loop.
Chain of Thought
A prompting technique that instructs the model to break down complex problems into sequential reasoning steps before producing a final answer. Chain of thought significantly improves accuracy on math, logic, and multi-step tasks.
Tree of Thought
An advanced reasoning framework where the model explores multiple solution paths simultaneously, evaluates each branch, and selects the most promising approach. Tree of thought enables more thorough problem-solving than linear chain-of-thought reasoning.