Location intelligence company Local Logic has announced the availability of the Local Logic MCP, a hosted server that connects AI applications directly to verified location data through the open Model Context Protocol.
According to a release, the Local Logic MCP allows compatible AI models to retrieve measured information about neighborhoods and individual locations when responding to a question. Its read-only tools give AI applications access to Local Logic’s location intelligence through a single connection, helping real estate companies build more accurate search, chat, analysis and decision-support experiences.
“AI models are very good at producing plausible answers, but the location facts that influence real estate decisions are not reliably captured in their training data,” said Gabriel Damant-Sirois, co-founder and chief product officer at Local Logic. “The Local Logic MCP gives models a direct connection to measured information at the moment they need it. That allows teams to improve accuracy without depending on increasingly large and expensive models.”
Study measures the effect of grounding on AI accuracy
Local Logic said the company evaluated seven language models on 490 neighborhood questions spanning 46 neighborhoods in 39 cities. Each model answered the same questions twice: Once using its training data alone and once with access to Local Logic data through the MCP.
Every factual claim was checked against live data rather than a predefined answer key. Across the evaluation, researchers scored 153,530 claims and verified 7,350 answers, the company said.
The study found that grounding models with Local Logic data:
- Increased the number of verified facts per answer by up to 2.3 times.
- Reduced contradicted claims by roughly two to four times per model.
- Reduced the share of answers containing multiple serious errors from approximately 32% to 17%.
- Enabled GPT-5 nano to reach 97.6% accuracy on the product categories, slightly exceeding the 96.9% result achieved by an ungrounded GPT-5 at roughly one-seventh of the cost per answer.
- Produced stronger results than a dedicated live web-search product, which reached 82% accuracy on the same questions.
The largest improvements occurred among smaller and less expensive models, the release stated, demonstrating that access to authoritative data can have a greater effect on factual accuracy than model size alone.
The complete methodology and findings are available in Local Logic’s technical research paper.
Built for real estate applications
The company said Local Logic MCP is designed for companies putting AI in front of consequential real estate decisions. Applications include:
- Grounded neighborhood search and conversational experiences
- Similar-neighborhood and affordable-alternative discovery
- Agent and loan-officer assistants
- Listing and member content generation
- Underwriting and site-selection analysis
- Market and location research
Commute routing and climate-risk capabilities are scheduled to become available through the MCP next, the company stated.
The MCP is already being used in production by Infinityy to ground answers in its AI-powered real estate chatbot. Local Logic has also used the MCP internally to build Ask Local Logic, a consumer experience that allows people to ask natural-language questions about neighborhoods and locations.
Because the service is hosted, companies do not need to install or maintain a local server. Developers connect an MCP-compatible client to a single URL, after which the model can select the appropriate Local Logic tools and retrieve data as questions arise.
Additional information about the Local Logic MCP is available on the solution page, in the use-case library and in the technical documentation, the released noted.
Learn more at locallogic.co.
