"But without real context, AI's just guessing. It breaks when your tools change and hallucinates when your data isn't clearly mapped or accessible. Model context protocol (MCP) changes that. It creates a shared language between your model and your stack: structured, contextual, and built to scale. MCP enables you to stop shipping AI that acts smart and start building AI that is smart."
"Model context protocol is a framework or guideline used to define, structure, and communicate the key elements/context (prompts, conversation history, tool states, user metadata, etc.) to large language models (LLMs). It outlines the external factors influencing the model, such as: Who will use the model (stakeholders) Why the model is being created (objectives) Where and how it will be applied (use cases, environments) What constraints exist (technical, ethical, time-based, etc.) What assumptions are made about the real-world context"
Model Context Protocol (MCP) defines a structured framework for supplying large language models with essential external context such as prompts, conversation history, tool states, and user metadata. MCP specifies stakeholders, objectives, application environments, constraints, and real-world assumptions to ensure relevant and technically sound model behavior. Core components include validation criteria, purpose, scope, key concepts and variables, relationships and assumptions, and structural format. MCP creates a shared language between models and system stacks to reduce guessing, minimize hallucinations, and handle evolving tools or data. MCP supports scalable, reliable AI integrations and alternative implementation approaches such as ClickUp.
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