How to reliably connect LLMs to real-world data and systems
The Model Context Protocol (MCP), introduced by Anthropic in 2024, standardizes how large language models connect to external tools, databases, and systems, enabling more dynamic, agent-like behavior. While MCP streamlines integration by allowing LLMs to orchestrate tools and workflows, it carries risks if models do not fully understand the data they query. Approaches like GraphRAG may help improve context and reliability when connecting LLMs to real-world data.
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Pro How to reliably connect LLMs to real-world data and systems Opinion By Dominik Tomicevic published 30 April 2026 MCP Is great, but it isn’t the whole AI answer When you purchase through links on our site, we may earn an affiliate commission. Here’s how it works. How to reliably connect large language models (LLMs) to real-world data and systems, and why approaches like GraphRAG can help. (Image credit: Getty Images) Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Threads Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Subscribe to our newsletter The model context protocol (MCP), an open-source standard introduced by Anthropic in late 2024, standardizes how large language models (LLMs) connect to external tools,…
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