A team built a legal MCP server. Then they built a real estate MCP server.

Same architecture. Same distribution pattern. Same underlying workflow: connect an AI model to a domain-specific data source, expose it through a standard protocol, let professionals use it in their existing AI clients.

Legal first, then real estate. The same team walking the adjacency chain.

This is significant not because of what they built, but because of what they didn’t build. Between legal document workflows and residential real estate workflows, there’s a domain they skipped: commercial real estate due diligence.

Legal contracts, lease agreements, environmental reports, offering memoranda, rent rolls, title commitments — these are all professional document workflows. They all require extraction, validation, and synthesis. They all benefit from the same underlying AI capabilities. But they’re different enough in vocabulary and output format that building for one doesn’t automatically serve the others.

When a builder walks the chain domain by domain, they leave a map. The map shows which domains are already served and which ones are next. The domains ahead of you in the chain have already proven the workflow. The domains behind you haven’t been reached yet.

The useful question isn’t “has someone built this yet?” It’s “where in the chain does this domain sit?” Because the chain moves in one direction, and adjacent domains follow with a lag.

The team that walked from legal to residential real estate is showing you where they haven’t walked yet. Whether they build it next or someone else does, the direction is visible.

The map is the value. +++