Paris commercial premises from open data: shopfront history, street turnover, sourced figures.
io.github.IvandeMurard/paris-compass-mcp MCP Server
This MCP server provides access to Paris commercial premises data from open data sources. It focuses on shopfront history and street-level dynamics, including street turnover and sourced figures, to support geospatial analysis rather than only single-unit attributes.
🛠️ Key Features
Shopfront history
Street turnover
Sourced figures for the referenced data
🚀 Use Cases
Datavisualization of commercial premises change over time
Geospatial workflows for real-estate and smart city context
TypeScript-based tooling that consumes open data for maps and analysis
⚡ Developer Benefits
Topics align with geospatial, map, and datavisualization use cases
Uses a focused data scope tied to Paris intra-muros commercial-property context
⚠️ Limitations
Readme excerpt indicates the approach emphasizes the surrounding street context; details beyond shopfront/street data are not described in the provided source material.
Open the demo → · no account needed · pre-recorded addresses, 17th arrondissement
For an agent →npx -y paris-compass-mcp · six tools, read-only, on the hosted corpus
The question
You are standing in front of an empty shopfront. The agent says the street is lively.
Compass answers on six axes at once.
What was here
10 rue Mandar, 2e: an Asian restaurant in the 2023 census, and six goodwill sales at that street number since 2017
How the street behaves
turnover and survival by trade, measured across three censuses — withheld until the 2017 and 2020 licences are cleared
What it costs
median fonds between 160 000 and 170 000 €, 220 000 € for a café
What is moving now
an insolvency filed here is public months before any listing
What is around it
schools, healthcare, food, parks and transit counted within 800 m, aggregated into walkability
What the environment is worth
air quality measured (Copernicus), natural and technological risks within 1 km, noise modelled from major roads at 500 m
Each of those arrives with its source, its date and its confidence level.
Note the wording, because the product keeps it too: air quality is measured, noise is modelled. Noise and footfall are proxies, labelled as such on screen and here.
Three that deserve more than a line
Has a kitchen ever been here?
If a restaurant occupied the unit, the extraction, grease trap and power are probably already in. If not, creating them runs into tens of thousands and needs the building's agreement. The most expensive question this data answers — before you travel.
Is this a graveyard or a good street?
A street that churns is not a street that dies: units can turn over and refill. And turnover only means something against its own trade, and against its own quartier — the same trade does not hold equally from one neighbourhood to the next. Compass measures both across three censuses.
Those figures are not printed here. They rest on the 2017 and 2020 vintages, whose licence has not been cleared, and the server withholds them from an anonymous caller. This page follows the same rule as the product: a rate derived from a withheld vintage is withheld too.
What did people actually pay?
25 661 goodwill sales published with their price since 2015. For the 5 971 tied to a single shopfront, the median Paris fonds changes hands between 160 000 and 170 000 € — a range, not a point, because goodwill prices are declared in round numbers: the median sits on a step and moves in jumps, so a single figure would be precise about something the source is not. The trade decides almost everything.
Food shop
Café / restaurant
Clothing
Personal services
250 000 €
220 000 €
86 000 €
50 000 €
One address makes the point. At 10 rue Mandar, trace_premise returns six goodwill sales between 2017 and 2024, from 87 500 to 450 000 € — retrieved 7 October 2026. Each one is probable, not established: two shopfronts share that street number, and the notice does not say which was sold. The 2017 and 2020 census rows come back withheld, the 2023 one established.
How a number earns its place
Every figure arrives with its source, its licence, its date — and how sure it is.
flowchart LR
A["APUR BDCom<br/>3 censuses"] --> M["Address<br/>matching"]
B["BODACC<br/>sales + insolvencies"] --> M
C["INSEE Sirene<br/>establishments"] --> M
M --> V{"Does the source<br/>name THIS unit?"}
V -->|"names it directly"| E["established"]
V -->|"two sources agree,<br/>neither names it"| F["corroborated"]
V -->|"address shared by<br/>several shopfronts"| G["probable"]
V -->|"source is silent"| H["undetermined"]
E --> O["Every figure carries<br/>source · licence · vintage · method"]
F --> O
G --> O
H --> O
O --> UI["Map and unit file"]
O --> MCP["MCP server,<br/>for an agent"]
Four levels, never a percentage. A confidence score out of 100 would be exactly the kind of unverifiable number this product refuses. The level is computed from columns that already exist, and every row carries the reason that produced it.
Composition across the corpus, measured 6 October 2026 — this is the quality metric, and improving means moving these four numbers leftward:
established
corroborated
probable
undetermined
51.2%
5.9%
37.0%
5.9%
That 37.0% is structural, not laziness: BODACC names an address, BDCom names a unit, and 69% of units share their street number. No public data will say which of eight shopfronts was sold.
A gate runs the whole corpus against 56 invariants, 24 frozen baselines and 8 hand-verified chronologies before anything ships, and again every day on a schedule — invariants counted 6 October 2026 with grep -c '^-- @invariant ' eval/invariants.sql. Most of them check what the functions return; one checks what they are — a function exposing an observed column must be SECURITY DEFINER, because row-level security silently turns a withheld row into "never surveyed".
The badge above counts invariants; it does not say the gate passes. The reds currently open are the issues labelled porte-rouge.
Two founding constraints
Most commercial-property tools describe the unit — floor area, rent, photos — and leave you to infer the rest. Compass sells neither coverage nor granularity: it sells interpretation, and context is the product.
An interpretation is worth only what you can check. The tools in this market promise answers grounded in audited data — that names who verified. Compass promises figures re-derivable from a cited public source — that names who can verify: you. An internal audit is a promise. A cited source is a test anyone can rerun, including against us.
If a number cannot be re-derived from a cited public source, it is not shown.
No landlord declarations, no scraped listings, no proprietary estimate, no score invented to fill a gap. Missing data is displayed as missing — n/a, never 0.
If two units on the same street get the same verdict, Compass has said nothing.
The useful granularity is the street segment, sometimes the side of the pavement. An indicator that does not vary at that scale describes general context; it does not settle a decision.
Who it is for
The entrepreneur — the shopkeeper, restaurateur, craftsperson or franchisee who decides where to open. Once or twice in a working life, on a lease that binds for years.
An agent — an LLM asking the same question through an MCP server. Same scoring core, same traceability requirement, different output: JSON and a chain of thought instead of a map.
Not brokers. A broker qualifies dozens of locations a month and needs portfolios, bulk comparison, national coverage. Every one of those makes the product heavier for someone studying a single address in depth. Serving both serves neither — a broker who wants Compass gets what the agent gets: the API.
What it refuses
Every refusal buys something back. The trade is the point.
It refuses
What that buys
A listings portal
Position upstream of the listing. Compass shows what is coming free — ceased trading, goodwill sold, court-wound-up, censused empty — not what everyone already sees
A rent estimate
Honesty about the one number everybody wants. No open dataset of actual commercial rents exists in France
A revenue forecast
Credibility. Inventing it would poison every other number on the page
A single score out of 100
A bakery wants footfall, a yoga studio wants quiet, a wine merchant wants median income. One score averages away what pulls against itself
National coverage
Depth. The sources that carry the value are local
An account to explore
Nothing to get past before finding out whether the tool is any use
What it cannot answer, and why — the gaps shape the product more than the features do
What rent will I pay?
No open observatory of commercial rents exists in France. Local rent observatories cover private housing; INSEE's ILC is a revision index, not a level. Street-level commercial values are sold by private vendors — which is the proof they are not open. Goodwill sale prices carry an indirect signal and nothing more.
How many people walk past this door?
Paris has no permanent pedestrian sensor: the city's multimodal counters cover bikes, scooters, motorcycles, cars, lorries and buses — not pedestrians.
Vendors do sell the number, and "proprietary and expensive" is the weak objection — worth being precise about what it actually is. It comes from a panel of mobile handsets: SDKs embedded in third-party apps report GPS coordinates in the background, and the sample is then extrapolated by weighting it against known demographics. The vendors say so themselves — "We don't see everyone. We see a sample." Peer-reviewed work on a comparable panel measured a mean sampling rate of 7.5%, swinging between 4.5% and 14.5%, with low-income and less-educated populations under-represented and the urban/rural bias reversing sign mid-series.1
So the figure is modelled, never measured — and the buyer cannot see the panel size on their own street, cannot replay the weighting, and is not told when a recalibration moves last year's number. The objection is not honesty, it is verifiability. Compass measures presence and rhythm from open sources instead, and labels it a proxy on screen.
1 Li Z., Ning H., Jing F., Lessani M. N. (2024). Understanding the bias of mobile location data across spatial scales and over time. PLOS ONE 19(1):e0294430.
What do people here spend, and on what?
Card transaction data: proprietary. No workaround.
Which units are on the market today?
Commercial listings live on private portals whose terms forbid reuse. Which is why Compass works upstream of them instead.
Status
Honest labels, in the sense that built means the code runs and the gate passes — not that it is deployed.
Component
State
Map, neighbourhood scoring, environment panel
Demo — pre-recorded addresses in the 17th arrondissement, not yet wired to the ingested corpus
Provenance surfaced on every figure
Built
BDCom ×3 · BODACC · Sirene · geography
Built — 85 418 units, 228 275 census records
Deployed to the hosted database
Live since 15 August 2026 — read anonymously with the publishable key
Premise history in the browser — BDCom ×3 and BODACC on one timeline
Built — demonstrated against the hosted database in a dev browser, not in the demo. docs/tickets/w0-fiche.md
Exportable one-address file
Design, next up
MCP server for agents
Published and listed — npx -y paris-compass-mcp (npm, MCP registry), six tools, anonymous read-only, nothing to configure.
Agent self-assessment of its own confidence
Research
Where every number comes from
Connected today
Source
Producer
Use
Licence
OpenStreetMap (Overpass)
OSM contributors
Vacant and occupied units, retail, schools, healthcare, parks, transit, roads
ODbL
Base Adresse Nationale
Etalab / IGN
Geocoding
Licence Ouverte 2.0
Rent control dataset
Ville de Paris / OLAP
Housing only — a catchment-area signal, never presented as a commercial rent, never multiplied by a floor area, never a filter
ODbL
CAMS Europe (Open-Meteo)
Copernicus
AQI, PM2.5, NO₂
CC BY 4.0
Géorisques
BRGM / MTE
Natural and technological risks within 1 km
Licence Ouverte 2.0
Scores computed client-side: walkability, transport access, density per category, noise and footfall — the last two explicitly labelled estimates, for lack of a reliable open source.
Ingested in the hosted database, not yet in the demo
Source
Producer
Contribution
BDCom 2017 / 2020 / 2023
APUR
Door-to-door census of every ground-floor unit with a shop window, 224-activity nomenclature, floor-area bands. The unit identifier is stable across vintages, so three censuses give turnover measured against its own street, with previous lives as the evidence. Two limits are part of the claim: vacancy is measurable on 2017 and 2020 only — the 2023 layer carries retail alone, so a missing unit is "no longer a shop", not "empty" — and the licence differs by vintage, so BDCom cannot be announced as ODbL across the board
BODACC
DILA
Goodwill sales with their price, and insolvency proceedings — the closest public figure to what an entrepreneur will pay, and a signal that a unit is coming free
Sirene geolocated
INSEE
Corroboration: places an establishment of the same company at the address, which raises a notice to corroborated without ever making it established
PLU commercial protections (plub_protcom)
Ville de Paris
On a protected linear, a ground-floor unit cannot change use — the first thing that can kill a project. Informational only, no regulatory value: the Portail des Règles d'Urbanisme is the authority. Version voted by the Conseil de Paris 20 November 2024
Planned
Source
Producer
Contribution
Mobiliscope
CNRS
Population actually present hour by hour — distinguishes an office district that triples at noon from a residential one
Transit validations
Île-de-France Mobilités
Counted entries per station, hourly since 2015 — replaces the footfall proxy with a measured number
DVF
DGFiP
Sale prices of the walls per m²
INSEE IRIS / FiLoSoFi
INSEE
Population, income, socio-professional categories
GTFS IDFM
Île-de-France Mobilités
Real travel times and frequencies
Bruitparif
Bruitparif
Modelled and measured noise, replacing the road proxy
Sitadel
SDES
Planning permissions — future residents and future competitors, two years ahead
Scope
Paris intra-muros first, Île-de-France next. Depth over breadth: the sources that carry the value are local.
src/core/ is pure — no fetch, no React, no DOM. That is what lets the same scoring serve a browser, a test runner and an MCP server.
Run it locally
Node.js 18+.
sh
git clone https://github.com/IvandeMurard/paris-compass.git
cd paris-compass
npm install
npm run dev
Served at http://localhost:8080.
Script
Does
npm run dev
Vite dev server with HMR
npm run typecheck
tsc --noEmit
npm run test
Vitest
npm run eval
The gate: invariants, baselines, golden cases
npm run build
Production build
Open data sources need no key. For your own backend, put VITE_SUPABASE_URL and VITE_SUPABASE_PUBLISHABLE_KEY in .env.local. Only VITE_-prefixed variables reach the browser — never put a service-role or paid key there.
That distinction matters more than it looks on a product built entirely from open data. The licence covers what is in this repository — the scoring core, the ingestion pipeline, the interface. It grants nothing over the datasets, which keep their own terms:
Requires
ODbL — OpenStreetMap, rent control dataset, BDCom 2023
Licence Ouverte 2.0 — Base Adresse Nationale, Géorisques, Sirene
Naming the source and its update date
CC BY 4.0 — CAMS Europe / Copernicus
Attribution
BDCom 2017 and 2020 are not redistributable at all. Their licence differs from the 2023 vintage, which is why the database carries a publicly_redistributable flag per vintage and withholds their content from an anonymous caller — content and absence alike, so that withholding leaks nothing either.
This repository contains no data extract, and must not. It contains the code that fetches, models and cites the data.
Interested in the approach, or in the MCP layer? Open an issue.
Install
Configuration
Environment variables
SUPABASE_URL
Optional. The server carries the public Compass project, so nothing is required to run it. Set this only to read a different Supabase project — a derived deployment.
SUPABASE_ANON_KEY
Optional, and never a service key. The anonymous publishable key, read-only under row level security — the same trust boundary a visitor of the website has.