Kill local-fact hallucinations

Why killing local-fact hallucinations is a worthy use case

If you are building an AI product on top of real estate, location questions are your highest-risk surface. The facts that decide a home: what is nearby, which line the train is on, how walkable a block really is, are not reliably in any model’s training data, cannot be crawled cleanly, and get less accurate exactly in the long-tail neighborhoods where a large share of transactions happen. A model will still answer, fluently and with total confidence. That confidently wrong answer is the single fastest way to lose a user’s trust in your product.

The Local Logic MCP is the retrieval layer that removes that risk. It exposes measured location data as read-only tools your model calls mid-response, so it reads real values instead of reaching for them. For a proptech or AI team, it replaces the integration you would otherwise build and maintain, and it returns an honest “no data” at the edge of coverage instead of a plausible guess, which is exactly the behavior a production system needs.

How to build it

1. Add the MCP as a tool layerConnect the hosted server through the OpenAI Responses API, the Anthropic Messages API, or any MCP-compatible client. One URL, one key — no data pipeline to build.
2. Route location intent to toolsWhen a prompt touches a place, have the model call search_geographies, then the specific tools the question needs — get_scores, get_neighborhood_pois, get_neighborhood_profile.
3. Constrain the model to returned dataSystem-prompt the model to answer only from tool output and to say when a fact is not covered. This is what converts “sounds right” into “is right.”
4. Evaluate the liftRun your own before/after eval on location prompts. Grounding is measurable: track verified facts per answer and the rate of materially wrong claims.
5. Ship with graceful fallbacksTreat “no data” as a first-class response in your UX, so the product stays trustworthy at the edge of coverage.

See it in action

The clearest test of a hallucination fix is a specific, checkable question. Here a generic model is asked about amenities and transit in Capitol Hill, Seattle — and then the same model grounded on the Local Logic MCP. Every grounded detail is a live response.

The scores behind the answer

Why web search is not a substitute

The obvious question is why not just let the model search the web. We tested it. A dedicated live-retrieval product reached only 82% claim accuracy on the proprietary location categories — below every model grounded on Local Logic data. The moat is the data, not the retrieval: we build and calibrate location attributes that cannot be reconstructed from crawling. Across the full evaluation of 7 models on 490 questions in 46 neighborhoods, grounding delivered up to 2.3x more verified facts per answer and roughly halved the answers with serious errors.

The payoff

For an AI builder, grounding is the difference between a demo that impresses and a product that survives contact with real users. You ship location answers you can trace to a source, you cut the hallucinations that quietly erode trust, and you do it with one connection instead of a data-engineering project — getting the full breadth of Local Logic’s data rather than wiring up endpoints one at a time.