LlamaIndex MCP

LlamaIndex is a framework for building retrieval and document workflows on top of private data, and its MCP layer connects that stack to any client that speaks the Model Context Protocol. Instead of writing a bespoke integration, an agent points at an MCP endpoint and gets typed access to the indexes, parsers, and extraction pipelines a team has already built, so answers come from the team's own documents rather than the model's memory. Two directions matter here. Outbound, a LlamaIndex agent can consume external MCP servers as tools, which turns any MCP catalog into callable functions inside a retrieval workflow. Inbound, a LlamaIndex workflow or query engine can be published as an MCP server, so a chat client or coding assistant can run semantic search over a knowledge base, pull the matching source passages, and extract structured fields out of PDFs and other messy files without leaving the conversation. It fits teams that already run retrieval-augmented generation in production and want their assistants querying the same index the application uses. The common pattern is one server per knowledge domain, such as support docs, contracts, or research notes, with each response carrying source references so the agent can cite what it actually retrieved.