Ground your site-selection AI on the Local Logic MCP to screen locations from measured walkability, real nearby businesses, and objective catchment demographics instead of guesswork.
Why grounded site selection is a worthy use case
Commercial and retail site selection runs on location facts: how walkable a corridor is, what is already there, and who the surrounding catchment is. Teams increasingly ask AI to screen candidate sites, but a generic model cannot reliably produce walkability, amenity density, or catchment demographics for a specific block. It will still answer, and a confident wrong read on a site can send a real-estate decision in the wrong direction.
The Local Logic MCP grounds site screening in measured data. It gives an AI verified location scores, the actual points of interest along a corridor, and objective catchment demographics on demand, so teams can shortlist and compare sites from calibrated data instead of guesswork, and reach a human review faster.
What good looks like
Every site read is backed by measured walkability, real nearby businesses, and objective catchment data, so the shortlist is defensible before anyone visits.
How to build it
- Connect the MCP to your screening workflowPoint your site-selection assistant or internal analysis tool at the hosted MCP with your MCP key, through the OpenAI or Anthropic APIs or any MCP-compatible client.
- Resolve the candidate areaCall
search_geographiesto turn a corridor or neighborhood into a stablegeog_id. - Pull the screening signalsCall
get_scoresfor walkability and vibrancy,get_neighborhood_poisfor the businesses already on the ground, andget_demographicsfor objective catchment context. - Compare and shortlistHave the model score sites against your criteria from the returned values only, and flag where data is thin instead of guessing.
- Hand off to human reviewUse the grounded shortlist to focus site visits and underwriting on the strongest candidates.
See it in action
A site-selection assistant screens the Damen and Milwaukee corridor in Wicker Park, Chicago, for a premium cafe or retail concept. Here is the generic answer versus the same model grounded on the Local Logic MCP. Every grounded figure is a live response.
Behind the scenes: MCP tool calls
✗ Generic model, ungrounded
“Wicker Park is a decent area with moderate foot traffic. Median income is around $70,000 and it skews suburban families, so a cafe could work.”
The catchment read is invented and roughly half the real income, and the corridor is neither low-traffic nor suburban.
✓ Grounded on Local Logic MCP
“Strong candidate. The corridor is maximally walkable (10/10) with exceptional cafe (9.9/10) and retail (9.2/10) density, alongside anchors like Big Star and La Colombe. The catchment has a median household income of $152K and a median age of 32, an affluent, young base.”
A defensible read before anyone visits the block.
The corridor at a glance
The catchment, quantified
| Objective catchment metric | Wicker Park |
|---|---|
| Median household income | $151,903 |
| Median per-capita income | $92,489 |
| Median age | 32 |
| Households | 8,578 |
| Households without children | 83% |
The payoff
Grounded screening compresses the first pass of site selection from days of manual research into a sourced, comparable read on every candidate. Teams shortlist from measured walkability, real on-the-ground businesses, and objective catchment data, then spend their site visits and underwriting on the locations most likely to work.
Frequently asked questions
How does grounding help site selection?
It lets an AI screen candidate sites from measured walkability, the actual points of interest along a corridor, and objective catchment demographics, so shortlists are defensible before anyone visits.
What signals does it use?
Location scores for walkability and vibrancy, get_neighborhood_pois for nearby businesses, and get_demographics for objective catchment context such as income and age.
Is it a replacement for site visits?
No. It compresses the first-pass screening so teams can focus visits and underwriting on the strongest candidates, with a human making the final call.
How accurate is the catchment data?
Demographics are objective, measured figures rather than model guesses, so the catchment read reflects real data for the area.
What coverage is available?
Location scores, points of interest, and demographics cover the US and Canada, through one hosted connection.
Ground this use case on your own market
Bring us your query mix. We will show you the grounded difference live, on the neighborhoods you care about.
At a glance
- Best forCommercial Real Estate
- WhoCRE & site-selection teams
- CoverageUS & Canada
- SetupOne URL + MCP key
MCP tools used
search_geographies
get_scores
get_neighborhood_pois
get_demographics
