The Local Logic MCP is here: verified location data for any AI

Everyone in real estate now has the same AI, and it is confidently wrong on the questions that decide a home. Our MCP connects any model to Local Logic’s verified location data, so it answers from measurement, not from memory.

Ask a popular AI tool what a neighborhood is like to live in, and you get a fluent, confident answer in seconds. In our research, models invented transit lines, parks, or even schools that don’t exist, and never hedged. When every team is building on the same handful of models, sounding right is easy, but being right is the hard part. And it is the only part that earns trust.

Today we are making the Local Logic MCP available. It is a hosted server that gives any AI application a direct connection to our verified location data, exposed as read-only tools the model can call on demand. There is nothing to install. You point an MCP-compatible client at one URL, and the model looks up the measured answer, then responds. We call this grounding, and we measured how important it is.

In a formal evaluation, connecting models to Local Logic data delivered up to 2.3x more verified facts per answer, and sharply cut the invented ones, by up to about 5x on the cheaper models. A small, inexpensive model grounded in our data reached 97.6% accuracy on neighborhood questions, edging out a frontier model’s own 96.9%, at roughly 1/7 of the cost per answer.

You do not need the biggest model. You need the right data feeding it.

Already in production

We built the Local Logic MCP for teams using AI to support real-world decisions, and they are already putting it to work. Infinityy is using it to bring accurate, verifiable neighborhood intelligence into Fin, its AI-powered assistant. The MCP’s built-in tools simplified implementation and helped Infinityy improve user experience, while a single connection provides access to the breadth of Local Logic’s data without the complexity of integrating individual endpoints.

“Infinityy is the decision-making layer for places and spaces, built for the moments between initial interest and a decision,” said Lisa Nickerson, CEO of Infinityy. ” Providing buyers and renters the right information at the moment questions arise makes the difference between a prospect continuing to engage or moving on. With the Local Logic MCP, we provide Fin access to verifiable neighborhood intelligence through a single connection, helping real estate professionals give buyers and renters the information they’re looking for, when they’re looking for it, and move more conversations toward a purchase or lease.”

What you can build

The same connection powers a range of use cases: grounded neighborhood search and chat, “show me somewhere similar” and affordable-alternative discovery, agent and loan-officer assistants, underwriting and site-selection analysis, and market insight.

We used our own MCP internally to build Local Logic Ask, a consumer platform where people can explore and compare neighborhoods using measured factors such as walkability, transit, schools, and parks. It shows what grounded neighborhood discovery can look like when those answers become an experience people can use.

Commute routing and climate risk are rolling out on the MCP next. Each use case answers a real question from verified data. Explore the full set in the MCP use case library.

Why it matters now

Location is the hard case for AI. The facts that decide a purchase are not sitting in the training data in any reliable way, they cannot be crawled, and models get less accurate exactly where it matters most, in the long tail of lesser-known neighborhoods where a large share of transactions happen. Grounding closes that gap on the categories we cover, and returns an honest “no data” when coverage runs out, instead of a confident guess.

See it and use it

Heading to Blueprint Vegas September 22–24? Bring the neighborhood question your AI cannot afford to get wrong. Book a custom demo with our team and see how the Local Logic MCP answers it from verified data.

• Explore the MCP solution page
• Browse the use case library
• Read the technical docs
• Read the grounding research