Which integrations are available for this server?

Provides geocoding and points of interest search via Nominatim and Overpass API.

How do I use immo-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@immo-mcp What is the median price per m² in Bordeaux?" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

immo-mcp

by WardenPro

Overview Schema Related Servers Score Discussions

Python

Remote

immo-mcp

An MCP (Model Context Protocol) server for French real-estate market analysis, built exclusively on open data sources. No third-party site automation.

Data sources

Source	What it provides
DVF Etalab	All notarised property transactions in France (2018-present)
OpenDataSoft DVF API	Same DVF data, queryable without a local DB
ADEME DPE-v2	Energy-performance certificates (DPE)
Géorisques	Flood, seismic, radon, ICPE risk data
CRE	Regulated electricity tariffs
INSEE	Postal code → INSEE commune lookup
Nominatim / Overpass OSM	Geocoding and points of interest

Related MCP server: Property Comparable Sales MCP Server

MCP tools

Tool	Description
`dvf`	Search DVF transactions by city / postal code / department
`dvf_stats`	Aggregate price statistics from DVF (median, p25/p75, …)
`dvf_db_info`	Report on the local DuckDB DVF database
`pappers_immo`	DVF statistics via the OpenDataSoft public API
`detect_underpriced`	Compare a market segment against DVF + ODS reference prices
`dpe`	Search DPE certificates by city or postal code
`georisques`	Risk analysis for a location (flood, seismic, radon, ICPE)
`list_georisques_kinds`	List available Géorisques risk categories
`electricity_tariff`	Fetch current regulated electricity tariffs (CRE)
`insee_lookup`	Resolve a postal code to its INSEE commune codes
`geocode`	Forward geocode an address via Nominatim
`pois`	Search points of interest around a location (OSM Overpass)
`list_poi_categories`	List available POI category presets
`listings_stats`	Compute price statistics on a list of listings
`listings_gap`	Annotate listings with gap-from-median percentage

Quick start

1. Install

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

2. (Optional) Build a local DVF database

For faster, offline DVF queries you can build a local DuckDB:

# Requires Docker
cd docker/dvf
DVF_HOST_DATA_DIR=~/.immo-mcp/dvf docker compose up

The MCP server will look for the database at ~/.immo-mcp/dvf/dvf.duckdb. Without it, the dvf tool falls back to the OpenDataSoft API automatically.

3. Start the server

python -m immo_mcp.server

4. Connect from Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS):

{
  "mcpServers": {
    "immo-mcp": {
      "command": "/path/to/.venv/bin/python",
      "args": ["-m", "immo_mcp.server"]
    }
  }
}

Environment variables

Variable	Default	Description
`DVF_DB_PATH`	`~/.immo-mcp/dvf/dvf.duckdb`	Path to local DuckDB DVF file
`IMMO_CACHE_DB`	`~/.immo-mcp/listings.db`	SQLite listing cache

Development

pip install -r requirements.txt
pytest

License

MIT — see LICENSE.

This server cannot be installed

license - permissive license

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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