Footstep AI

Name: Footstep AI
Author: footstep

Footstep gives agents the location layer they need to act in the real world. Turn place names and addresses into coordinates, plan and optimize multi-stop trips, get turn-by-turn directions, build travel-time isochrones, search for points of interest, snap GPS traces to roads, and predict where someone could reach within a given time (_research preview - contact support@footstep.ai for access_) . Every response is structured JSON designed for LLM consumption.

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模型生成摘要（rules/v1 · 2026-09-27 16:15）：Footstep gives agents the location layer they need to act in the real world
这是模型对公开材料的总结，不是官方声明，请以原链内容为准。

接入信息

传输形态: http
鉴权方式: 鉴权未知
端点: https://footstep.run.tools

鉴权方式未标注，请核对官方文档后再接入——不要直接使用以下片段

{
  "mcpServers": {
    "Footstep AI": {
      "url": "https://footstep.run.tools"
    }
  }
}

能力清单

工具	说明
get_directions	Get driving, walking, or cycling directions between locations. Returns distance, duration, turn-by-turn steps, and terrain analytics (elevation, difficulty). Optionally include a natural-language summary. Geometry for the route is delivered via the `render` envelope as a GeoJSON LineString layer.
optimize_stops	Find the fastest order to visit multiple stops. Returns the optimised route with distance/time savings compared to the original order. Geometry is delivered via the `render` envelope: a LineString for the route and a Point layer for the ordered stops (each carrying an `order_index` property).
compare_routes	Compare 2-4 travel modes between the same locations. Returns a side-by-side comparison of distance, duration, and terrain difficulty with a natural language summary. Geometry is delivered via the `render` envelope: one LineString layer per mode, each carrying `mode` and `recommended_color` style hints so a renderer can colour them distinctly.
find_and_route	Geocode an origin AND a destination AND compute a route between them, in one call. Use this when the user wants directions between two places given by name — 'directions from A to B', 'route from A to B', 'take me from A to B'. Do NOT use this for 'Where is X?' / 'Find X' — those have no origin and want only the destination plotted; use `geocode` instead. Both `origin` and `destination` accept either lat/lon coordinates OR a place name / postcode / address (which is geocoded internally). Returns the route as a `render` envelope LineString plus Point layers for the matched origin and destination.
get_isochrone	Find everywhere reachable within a time or distance limit from a starting point. Use this for 'what's reachable from X in Y minutes' / 'where can I get to from here' queries. Pass `from` as a place name (e.g. 'Kings Cross, London') and the origin is geocoded for you, or pass `location` if you already have coordinates. Returns polygon boundaries for each contour. Geometry is delivered via the `render` envelope as one Polygon layer per contour, with `style_hints.contour_value` carrying the underlying numeric (minutes / kilometres) so renderers can colour by gradient.
get_matrix	Calculate travel times and distances between every combination of origins and destinations.
get_elevation	Get elevation data for a list of coordinates. Returns heights in metres above sea level and summary statistics (total ascent/descent, min/max elevation, all in metres).
snap_trace	Map-match a GPS trace to the road network. Returns the corrected path with confidence score, terrain analytics, and per-segment road attributes (surface, class, speed, grade). Geometry is delivered via the `render` envelope as a GeoJSON LineString of the snapped trace.
search_along_route	Find places along a route ranked by how little detour they add. Useful for finding stops (coffee shops, petrol stations, etc.) on the way. Geometry is delivered via the `render` envelope: a LineString for the route plus a Point layer for the candidate places.
geocode	Resolve a single address, landmark, or place name to coordinates. Use this when you specifically need the lat/lng of one place (e.g. to plot it, store it, or feed it to another system). For 'find X near Y' queries, prefer `search_places` with the `near` parameter — it geocodes internally. Returns ranked candidate locations with confidence scores in `results`. The `render` envelope contains ONLY the top match (one point) so a default render answers the question — disambiguation alternatives stay in `results` and can be rendered separately via `addLayer` if the user wants to see them.
reverse_geocode	Given coordinates (`lat` + `lon`), return the address or place label at that point. Use this when you have a location pin and need a human-readable description (e.g. labelling a map click). For 'what's near here' / 'find X near here' queries, use `search_places` instead.
batch_geocode	Geocode up to 1,000 addresses in one call. Returns coordinates and confidence for each.
search_places	Find points of interest near a location. `query` accepts both categories (cafe, restaurant, supermarket, park, gym, atm, pharmacy, hotel, hospital, fuel, library, museum, …) and specific venue names (Costa, Tesco, Black Sheep Coffee, Tower Bridge). Categories are expanded internally to multilingual synonyms so 'cafe' also catches 'coffee', 'café', 'kaffee', etc. Pass `near` as a place name (e.g. 'Kings Cross, London') and it's geocoded for you, or pass `lat`+`lon` if you already have coordinates. Returns ranked results sorted by distance from the search point.
parse_address	Parse and correct messy free-text addresses into structured components. Fixes typos, expands abbreviations (st → Street, ave → Avenue), and infers missing fields like postcode, region, or country. Returns the cleaned address, structured components, an itemised list of corrections, and a confidence score per row. Use this as a pre-step before geocoding when you have dirty input data; pass clean addresses straight to `geocode` or `batch_geocode`.
get_prediction	Generate spatial probability predictions for missing person scenarios. Given a search area, behavioural profile, and environmental conditions, returns per-hex scores across an H3 grid — telling you where to look first. Typically 5–10 seconds for cached areas; large or previously unseen search areas can take up to ~30 seconds while features are computed and cached. Wait for the response; do not retry or assume failure on slow turns. Input hygiene for callers: `profile` is required and SAR-critical — never guess it; ask the operator. `age_years` is optional — omit when unknown; the model handles missing age. `datetime` defaults to the time of the call upstream — pass an ISO 8601 string for any other time. Weather is fetched automatically from `datetime` unless overridden via `weather` or suppressed via `disable_weather: true`.

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