Add context to an AI task

Start with the row that matches the context you have:

What you have	Use	First action
A few known facts, no Oomira account	Keyless HTTP	Call `scaffold`
A saved Oomira account	MCP	Connect with OAuth and call `whoami`
Another person must answer	Interview link	Create an interview session

All three paths return Oomira Context Format (OCF): sourced, dated facts plus explicit gaps.

Generate context without an account

List the supported task schemas:

bash

curl -s https://oomira.com/api/v1/schemas

Choose a returned name, then send the facts you already know:

bash

curl -sX POST https://oomira.com/api/v1/scaffold \
  -H 'content-type: application/json' \
  -d '{
    "profile": {
      "name": { "first": "Jane", "last": "Doe" },
      "residency": ["CA-ON"]
    },
    "schema": "mortgage_application"
  }'

Use ocf as the task context. Ask the first blocking item in questions. For the next call, send the response's complete continuation object as continue_from with the new answer:

json

{
  "continue_from": {
    "schema": "mortgage_application",
    "profile": {
      "name": { "first": "Jane", "last": "Doe" },
      "residency": ["CA-ON"]
    },
    "answers": {}
  },
  "answers": { "property_target_value": 750000 }
}

Don't reconstruct or omit continue_from. The API is stateless.

Read saved context

Connect https://oomira.com/mcp with OAuth. Call whoami, then call about_me or about_world. If the saved context is incomplete, call list_schemas, then plan_interview.

To collect missing facts from the subject, call create_interview_session. Ask its returned questions in the conversation, render its MCP App, or send its interview_url.