Geo MCP Worker
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Geo MCP Workergeocode the Eiffel Tower"
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.
geo-mcp-worker
Geo MCP Server — Geospatial computation for AI agents.
Deployed on Cloudflare Workers. Powered by Nominatim / Overpass / OSRM. Free, no API key, stateless. English | 中文
Tools
Tool | Description | Source |
| Address text → lat/lon coordinates | Nominatim (OSM) |
| Lat/lon → address text | Nominatim (OSM) |
| Nearby POI search (20+ categories) | Overpass API (OSM) |
| Point-to-point distance & duration (driving/walking/cycling) | OSRM |
Protocol
MCP (JSON-RPC 2.0), compatible with search-mcp-worker.
POST /mcp— MCP endpointGET /health— Health check
Quick Start
Initialize
POST /mcp
{
"jsonrpc": "2.0",
"id": 1,
"method": "initialize",
"params": {
"protocolVersion": "2025-03-26",
"capabilities": {},
"clientInfo": { "name": "my-agent", "version": "1.0" }
}
}List tools
{ "jsonrpc": "2.0", "id": 2, "method": "tools/list" }Live API Examples
1. Geocode (geo_geocode)
Request:
{
"jsonrpc": "2.0", "id": 1,
"method": "tools/call",
"params": {
"name": "geo_geocode",
"arguments": { "address": "上海市徐汇区云锦路", "limit": 1 }
}
}Response:
{
"ok": true,
"query": "上海市徐汇区云锦路",
"results": [
{
"lat": 31.169501,
"lon": 121.453866,
"display_name": "云锦路, 龙华, 龙华街道, 徐汇区, 上海市, 200232, 中国",
"type": "residential",
"importance": 0.42
}
]
}School names work too:
{ "address": "向明中学浦江校区" }
→ lat=31.075575, lon=121.496283
→ "向明中学(浦江校区), 浦锦路, 浦锦街道, 勤俭, 闵行区, 上海市, 201112, 中国"⚠️ Does not accept company/brand names (e.g. "中电金信"). For such queries, use search-mcp first to get the street address, then pass it to geo_geocode.
2. Reverse Geocode (geo_reverse)
Request:
{
"name": "geo_reverse",
"arguments": { "lat": 31.169501, "lon": 121.453866 }
}Response:
{
"ok": true,
"display_name": "云锦路, 龙华, 龙华街道, 徐汇区, 上海市, 200232, 中国",
"address": {
"road": "云锦路",
"suburb": "龙华街道",
"city": "徐汇区",
"state": "上海市",
"postcode": "200232",
"country": "中国"
}
}3. Find Nearby POIs (geo_find_poi)
Subway stations:
{
"name": "geo_find_poi",
"arguments": {
"lat": 31.169501, "lon": 121.453866,
"category": "subway", "radius_m": 1000, "limit": 5
}
}Response:
{
"ok": true,
"count": 4,
"results": [
{ "name": "云锦路", "distance_m": 0, "category": "subway" },
{ "name": "龙华", "distance_m": 772, "category": "subway" },
{ "name": "龙耀路", "distance_m": 897, "category": "subway" },
{ "name": "龙华", "distance_m": 962, "category": "subway" }
]
}Restaurants:
{ "lat": 31.240168, "lon": 121.497945, "category": "restaurant", "radius_m": 500 }Yang's Dumplings — 223m
Morton's Grille — 319m | steak_house
Win House — 387m
Hooters — 457m | burgerSupported POI categories: restaurant, cafe, school, hospital, clinic, pharmacy, bank, atm, supermarket, convenience, subway, bus_stop, park, gym, cinema, library, kindergarten, police, fire_station, post_office, parking, fuel, marketplace
4. Route Planning (geo_route)
Driving:
{
"name": "geo_route",
"arguments": {
"from": { "lat": 31.169501, "lon": 121.453866 },
"to": { "lat": 31.240168, "lon": 121.497945 },
"mode": "driving"
}
}distance=12609m (12.6km) duration=14.7min confidence=highWalking (auto-calibrated for long distances):
{
"from": { "lat": 31.075575, "lon": 121.496283 },
"to": { "lat": 31.127125, "lon": 121.489319 },
"mode": "walking"
}distance=7352m (7.4km) duration=91.9min confidence=lowFor walking distances >2km, duration is recalculated at 80m/min (~4.8km/h) and flagged as
confidence=low.
5. Health Check
GET /health
{
"ok": true,
"name": "geo-mcp-worker",
"version": "1.0.0",
"tools": ["geo_geocode", "geo_reverse", "geo_find_poi", "geo_route"],
"data_sources": ["nominatim", "overpass", "osrm"]
}Search MCP Integration
Geo MCP handles spatial computation only. For semantic search, combine with search-mcp-worker:
User: "What subway stations are near 中电金信 Shanghai HQ?"
1. search_mcp("中电金信上海总部地址") → "上海市徐汇区云锦路XXX号"
2. geo_geocode("上海市徐汇区云锦路") → { lat: 31.17, lon: 121.45 }
3. geo_find_poi(lat, lon, category="subway", radius_m=1000) → 云锦路(0m), 龙华(772m)
4. geo_route(from=office, to=云锦路, mode="walking") → 3min walkDesign Constraints
Stateless: No CF KV. All data fetched from upstream APIs in real-time.
Zero auth: All upstream APIs are free and require no API key.
Coordinate precision: All coordinates truncated to 6 decimal places via
toFixed(6).Walking calibration: OSRM walking durations >2km are recalculated at 80m/min.
Semantic errors:
{ ok: false, reason: "poi_not_found", message: "..." }— agents can decide whether to retry with a larger radius or abort.
Deployment
# Upload via CF API
curl -X PUT \
"https://api.cloudflare.com/client/v4/accounts/<ACCOUNT_ID>/workers/scripts/geo-mcp-worker" \
-H "X-Auth-Email: <EMAIL>" \
-H "X-Auth-Key: <API_KEY>" \
-F "metadata=@/tmp/metadata.json;type=application/json" \
-F "index.js=@src/index.js;type=application/javascript+module"metadata.json:
{ "main_module": "index.js", "compatibility_date": "2026-04-08" }Expansion Roadmap
See GEO_TRANSIT_RESEARCH_REPORT.txt for the full survey of 28 projects.
Layer | Solution | Coverage | Status |
Layer 0 | OSRM + Nominatim + Overpass | Global driving/walking/POI | ✅ Deployed |
Layer 1 | Transitous | International public transit | 📋 Researched |
Layer 2 | Amap (高德) API | China public transit | 📋 Researched |
License
GPL-3.0
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