From connection to context: Dispelling the legal industry’s biggest myths about MCP

The legal sector’s use of AI is maturing, and its use is going beyond simply being an external chatbot or standalone tool.

The focus is shifting to embedding AI into everyday legal work, giving it access to the documents, matters, knowledge and systems lawyers use every day.

As firms increase their investment in AI technology, they need to look beyond what AI models can generate and focus on a more practical question: how do those models connect to the information lawyers rely on, where does that information sit, and how is access governed? That connection challenge is why Model Context Protocol (MCP) has started to attract so much attention.

Separating the standard from the assumptions MCP is an open standard that gives AI tools a more consistent way to connect to external systems, data sources and applications.

For all the excitement around MCP, firms need to be clear about what it does, and just as importantly, what it does not do.

Otherwise, there is a risk that firms either overestimate what MCP can solve on its own or dismiss it as just another technical acronym.

The reality sits somewhere in the middle.

MCP can help AI tools connect to the systems and data sources firms already use, but it does not automatically solve challenges around governance, integration, permissions or legal context.

With misconceptions starting to spread across the legal sector, here are five common myths to clear up.

Myth 1: MCP is only for Claude Although MCP was created by Anthropic, it is not just a Claude feature.

It is an open-source framework and is now becoming part of the broader conversation around how AI agents connect to external tools, data sources and systems.

That distinction matters for law firms and legal organizations generally.

If MCP is treated as a single-vendor feature, it can be dismissed as something tied to one model or product roadmap.

But as a broader connectivity standard, firms need to think about how it fits into their wider AI strategy, integration architecture and governance model.