Large language models (LLMs) like Anthropic’s Claude have unlocked massive context windows (up to 100k tokens in Claude 2) that let them consider entire documents or codebases in a single go. However, effectively providing relevant context to these models remains a challenge. Traditionally, developers have resorted to complex prompt engineering or retrieval pipelines to feed external information into an LLM’s prompt. Anthropic’s Model Context Protocol (MCP) is a new open standard designed to simplify and standardize this process. Think of MCP as the “USB-C for AI applications” — a universal connector that lets your LLM seamlessly access external data, tools, and systems. In this article, we’ll explain what MCP is, why it’s important for long-context LLMs, how it compares to traditional prompt engineering, and walk through building a simple MCP-compatible context server in Python. We’ll also discuss practical use cases (like retrieval-augmented generation and agent tools) and provide code examples, diagrams, and references to get you started with MCP and Claude. 

What is MCP and why does it matter?

MCP (Model Context Protocol) is an open protocol introduced by Anthropic in late 2024 to standardize how AI applications provide context to LLMs. In essence, MCP defines a common client–server architecture for connecting AI assistants to the places where your data lives — whether that’s local files, databases, cloud services, or business applications. Before MCP, integrating an LLM with each new data source or API meant writing a custom connector or prompt logic for that specific case. This led to a combinatorial explosion of integrations: M AI applications times N data sources could require M×N bespoke implementations.  MCP addresses this by providing a universal interface so that any compliant AI client can communicate with any compliant data/service server, reducing the problem to M + N integration points.