What Is MCP (Model Context Protocol)?
An open protocol that lets AI applications (like IDE assistants) connect to external tools and data sources through a standard JSON-RPC interface.
AI assistants are only as good as the context they can reach. MCP standardizes how an assistant discovers and calls external capabilities — a server declares tools (functions with schemas), and any MCP-compatible client can call them, no bespoke integration per pairing.
The practical impact for documentation is large: instead of an IDE assistant answering API questions from training data frozen months ago, it can call an MCP server that searches the vendor's live docs and answer from current content.
Because MCP is an open standard, one server serves every compatible client — Cursor, Claude, and the growing ecosystem — from a single endpoint.
How Kelu uses this
Every Kelu knowledge base can have its own MCP servers, with tools including search_<name>_knowledge_sources, ask_question, and list_sources — authenticated by client keys or the reader's sign-in and recorded in traces. Developers get your current docs inside their editor, not stale training data.
Related terms
RAG (Retrieval-Augmented Generation)
An AI architecture that retrieves relevant documents from a trusted knowledge source and passes them to a language model, so answers are grounded in real content instead of the model's training data.
Knowledge Base
A curated, indexed collection of an organization's knowledge — docs, wikis, tickets, repos — that an AI assistant retrieves from when answering questions.
llms.txt
A proposed standard for a markdown file at /llms.txt that gives AI tools a short summary of a site and links to its most useful pages, so they skip the clutter.
From definition to deployment
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