> ## Documentation Index > Fetch the complete documentation index at: https://docs.mireye.ai/llms.txt > Use this file to discover all available pages before exploring further. # Quickstart > Five minutes from zero to a cited answer about a US coordinate. This walkthrough makes three real API calls. No SDK, no install — just `curl`, `jq`, and a Mireye API token. We'll ask about a Manhattan rooftop, fetch the same location's flood-risk preset, and then wire up the hosted MCP server for Claude Code. ## 1. Pick a coordinate Use any US lat/lng. We'll use the corner of Beekman and Park Row in lower Manhattan, near City Hall: ``` lat = 40.7128 lng = -74.0060 ``` The accepted coordinate envelope is `lat ∈ [18, 72]`, `lng ∈ [-180, -65]` (lower 48, Alaska, Hawaii, US territories). Anything outside returns `400 coord_out_of_bounds`. ## 2. Get a token `/v1/ask` and `/v1/fetch` require a bearer token. Sign in at [www.mireye.com](https://www.mireye.com), create an API token in account settings, and export it: ```bash theme={null} export MIREYE_API_TOKEN="" ``` See [Authentication](/authentication) for the other credential paths (device-flow login for the local MCP adapter, OAuth for the hosted MCP endpoint). ## 3. Ask a question ```bash theme={null} curl -s https://api.mireye.com/v1/ask \ -H "Authorization: Bearer $MIREYE_API_TOKEN" \ -H 'content-type: application/json' \ -d '{ "lat": 40.7128, "lng": -74.0060, "question": "What is the elevation here?" }' | jq ``` Response: ```json theme={null} { "lat": 40.7128, "lng": -74.006, "question": "What is the elevation here?", "answered_at": "2026-06-12T07:29:37.353451+00:00", "answer": "The ground elevation at coordinate 40.7128, -74.006 is 13.15 meters above sea level (NAVD88 datum), per USGS 3DEP/EPQS at ~10m resolution. This elevation is above the 10-meter storm-surge exposure threshold, though proximity to the coast would warrant further review in conjunction with flood zone and coastal distance data for a complete flood-risk assessment.", "confidence": "high", "citations": [ { "source": "USGS_EPQS", "source_url": "https://epqs.nationalmap.gov/v1/json?x=-74.006&y=40.7128&wkid=4326&units=Meters", "fields": [ "elevation" ], "fetched_at": "2026-06-12T07:29:40.330095+00:00", "confidence": "high" } ], "fields_used": [ "elevation" ] } ``` What just happened: 1. The **planner** (Claude Sonnet 4.6) read the question, looked at the field catalog, and decided only `elevation` was needed. 2. The terrain layer fetched 3DEP elevation for the coordinate. 3. The **synthesizer** (also Claude Sonnet 4.6) wrote the prose answer, grounded in the fetched value, and the citation engine attached the source. Read the fields top-to-bottom: `answer` is the prose, `confidence` summarizes the whole response, `citations` is the audit trail, and `fields_used` lists exactly which catalog fields the answer depends on. ## 4. Fetch raw fields When you know what you want, skip the planner and hit `/v1/fetch`: ```bash theme={null} curl -s https://api.mireye.com/v1/fetch \ -H "Authorization: Bearer $MIREYE_API_TOKEN" \ -H 'content-type: application/json' \ -d '{ "lat": 40.7128, "lng": -74.0060, "fields": ["elevation", "coast_distance_m"] }' | jq ``` Response: ```json theme={null} { "lat": 40.7128, "lng": -74.006, "fetched_at": "2026-06-12T07:27:35.840477+00:00", "fields": { "elevation": { "value": 13.150006294, "unit": "meters", "source": "USGS_EPQS", "source_url": "https://epqs.nationalmap.gov/v1/json?x=-74.006&y=40.7128&wkid=4326&units=Meters", "confidence": "high", "fetched_at": "2026-06-12T07:27:35.826959+00:00", "dataset_vintage": "3DEP dynamic service", "ttl_seconds": 31536000, "notes": null }, "coast_distance_m": { "value": 764.2256402243065, "unit": "meters", "source": "NOAA_CUSP", "source_url": "https://shoreline.noaa.gov/data/datasheets/cusp.html", "confidence": "high", "fetched_at": "2026-06-12T07:27:35.839783+00:00", "dataset_vintage": null, "ttl_seconds": 31536000, "notes": null } }, "partial_failures": [] } ``` Each field is a self-contained record with provenance inline. The `partial_failures` array is empty here; on a call where the LCMS source times out, that field gets moved to `partial_failures` with `retryable: true` and the rest of the call still returns 200. Skip the field list entirely and use a preset for common workflows: ```bash theme={null} curl -s https://api.mireye.com/v1/fetch \ -H "Authorization: Bearer $MIREYE_API_TOKEN" \ -H 'content-type: application/json' \ -d '{"lat": 40.7128, "lng": -74.0060, "preset": "flood_risk"}' | jq '.fields | keys' ``` ```json theme={null} [ "coast_distance_m", "elevation", "intersects_nhd_area", "intersects_wetland", "nearest_waterbody_name", "nearest_wetland_distance_m", "surface_water_permanence_pct", "wetland_acres", "wetland_subtype", "wetland_type", "wetlands_within_100m_count", "wetlands_within_500m_count", "within_floodplain_polygon" ] ``` All presets and their field expansions are listed at [/api-reference/fetch](/api-reference/fetch#presets). ## 5. Use it from Claude Code If you previously configured a local stdio server named `mireye-earth`, remove it first: ```bash theme={null} claude mcp remove mireye-earth -s user ``` Add the hosted MCP endpoint: ```bash theme={null} claude mcp add --transport http --scope user mireye-earth https://api.mireye.com/mcp ``` Restart Claude Code, run `/mcp`, and complete the browser sign-in flow. Two tools are then available: `mireye_ask` and `mireye_fetch`. Ask Claude: > "What's the elevation at 40.7128, -74.0060?" Claude will call `mireye_ask`, get the cited answer back, and surface the citation in chat. See [/mcp/installation](/mcp/installation) for Cursor and custom-agent setup. ## 6. Self-discover the catalog Long-lived clients (agents, services) should fetch the catalog once at startup and cache it for an hour. This endpoint is public — no token needed: ```bash theme={null} curl -i https://api.mireye.com/v1/meta/fields | head -20 ``` The response sets `ETag` and `Cache-Control: public, max-age=3600`. Subsequent requests with `If-None-Match: ""` return 304. See [/api-reference/meta-fields](/api-reference/meta-fields) for the full flow. ## What's next The natural-language endpoint. Two-model planner + synthesizer. Fields, presets, and `partial_failures` semantics. `mireye_ask` vs `mireye_fetch` from an agent's perspective. A worked underwriting flow with real values.