chuofringer/placeroot

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**PlaceRoot grounds AI agents in open map data.** It's an MCP server that answers spatial questions — what's nearby, what's in this neighborhood, how do I get there — from Overture Maps open data. No API key, no signup, no vendor platform.
An independent project by vibemapper.
- 🎯 **Answers, not data dumps.** Every tool returns compact, ranked results sized for an agent's context window — never a raw GeoJSON dump.
- 🗺️ **Real routing, zero keys.** route and isochrone walk an actual street graph built from Overture's transportation segments — not a straight-line guess — anywhere on Earth.
- 🚶 **Reachability-filtered search.** find_places(..., within={"minutes": 15, "mode": "walk"}) keeps only results truly inside the street-graph walk/cycle/driveshed — not a radius that guesses at it, and not a second call to intersect a polygon yourself.
- 🏙️ **Rich, filterable place data.** Category, brand, confidence, operating status, contactability — sourced from Overture's open dataset (contributed by Meta, Uber, TomTom, and others).
- 📐 **Boundary-accurate.** Search inside a named place's real administrative polygon, not a guessed radius circle.
- ⚡ **Zero setup.** Reads Overture's public data directly — no key, no database, nothing to install beyond the server itself.
Quick start
Run it straight from PyPI or npm — no install step:
Add to Claude Code
Add to Claude Desktop
Add to your claude_desktop_config.json:
Add to Cursor / other MCP clients
Same config as above wherever your client keeps its MCP server list. Prefer npm? Use "command": "npx", "args": ["placeroot"].
Then ask your agent something spatial:
> *"What's around downtown Palo Alto?" · "Compare these two neighborhoods for a bike shop." · "Plan my errands: pharmacy, hardware store, post office."*
What it can do
**48 tools**, grouped into four families — every answer fits in a couple of thousand tokens:
| Family | Tools | Answers questions like |
|---|---|---|
| 🔍 **Search & identify** | find_places, find_near, geocode, reverse_geocode, place_details, search_categories, warmup_city, … | What cafés are near this point? Coffee near the Eiffel Tower? |
| 📊 **Area analysis** | summarize_area, compare_areas, neighborhood_verdict, summarize_buildings, land_use_at, transit_stops_near, … | What's in this neighborhood, should I live here, and how does it differ from that one? |
| 🚴 **Routing** | route, from_to, isochrone, optimize_route, places_along_route, distance_matrix, travel_time_matrix, map_match | How far to walk from A to B? What's reachable in 15 minutes? Best order for 6 stops? Which streets did this GPS trace actually take? |
| 🗺️ **Geometry & maps** | render_map, simplify_geometry | Show me this result as an interactive map |
It also ships seven **workflow prompts** (site selection, neighborhood comparison, errand planning, should I live here, get to know my city, verify listing claims, plan area visit) and three attachable **resources** — and a PLACEROOT_TOOLS setting to load only the tool profiles you need, cutting schema overhead by up to 95%.
📚 **Full tool catalog, prompts, resources & configuration → docs/REFERENCE.md**
> [!NOTE]
> Open data has honest limits: no live traffic, no opening hours, no ratings or photos — what PlaceRoot deliberately doesn't do. For everything else about where things are and what's reachable from them, it answers without a key.