Where to look first.
A probability heatmap of where a missing person is most likely to be found, built from their behavioural profile, the terrain, and current weather.
mcp.footstep.ai / predictWhy your agent needs this.
When the user asks the agent 'where might Mrs Patterson be?', the agent needs one tool that takes a behavioural profile, an area, and current conditions, and returns a usable probability surface. predict is that tool. Token-lean by default, render-ready by design. The agent picks it; you wrote none of the orchestration.
What your agent gets back.
- Returns a render envelope with one H3 hex layer per probability band.
- Probability scores conditioned on behavioural profile, terrain, and live weather.
- Token-lean: full hex grid is opt-in via include_hexes.
- Works in Claude Desktop, Cursor, Vercel AI SDK, OpenAI Agents, and any MCP-aware runtime.
- Composes naturally with parse_address for radio reports and get_isochrone for reach analysis.
Plug it into your agent.
Add Footstep to your MCP-aware runtime. Your model picks the tool at runtime; you wrote none of the orchestration.
"Generate a probability surface for a despondent missing person from the trailhead, 5km radius."{
"mcpServers": {
"footstep": {
"url": "https://mcp.footstep.ai",
"headers": {
"x-api-key": "sk_live_your_key_here"
}
}
}
}Related capabilities.
get_isochroneReachability polygons by time or distance from any starting point. Walking, cycling, or driving.
search_placesPoints of interest near a location, by category or by venue name. Sorted by distance.
parse_addressFree-text in, structured out. Fixes typos, expands abbreviations, infers missing fields, and scores confidence.
Ready to call it?
£5 free credit on signup. One auth header, every major runtime.