Mireye

/quickstart

Quickstart

Five minutes from zero to a cited answer about a US coordinate.

​ 1. Pick a coordinate

Python
lat = 40.7128
lng = -74.0060

​ Starting from an address?

curl -s https://api.mireye.com/v1/geocode \
  -H "Authorization: Bearer $MIREYE_API_TOKEN" \
  -H 'content-type: application/json' \
  -d '{"address": "350 5th Ave, New York, NY 10118"}' | jq

​ 2. Get a token

Shell
export MIREYE_API_TOKEN="<paste from dashboard>"

​ 3. Ask a question

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
  • The planner (Claude Sonnet 4.6) read the question, looked at the field catalog, and decided only elevation was needed.
  • The terrain layer fetched 3DEP elevation for the coordinate.
  • The synthesizer (also Claude Sonnet 4.6) wrote the prose answer, grounded in the fetched value, and the citation engine attached the source.

​ 4. Fetch raw fields

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

​ 5. Use it from Claude Code

claude mcp remove mireye-earth -s user

​ 6. Self-discover the catalog

cURL
curl -i https://api.mireye.com/v1/meta/fields | head -20

​ What’s next

POST /v1/ask

POST /v1/fetch

MCP tools

Insurance use case