read: fit frame.

Reads the frame of one fit search to The answer gives the markets ranked, the offering sculpted for each buyer profile, the tactics proposed to test the fit, the verdict for each market, and the set of options that compares them

Request body#

The request body is FitFrameInput.

Response#

The route returns a card whose body is FitSearchFrame. Every card ends with a foot that states the basis of each number, the scope of the call, and the time when the facts were true.

Headers#

A caller needs the brand rung and a token with the read grant. The platform or the desktop app serves this route.

Example#

The example sends the smallest body that FitFrameInput allows. The build validates it against the schema of FitFrameInput.

curl -X POST https://api.scaleintelligence.co/v1/read/fit_frame \
  -H "Scale-Key: $SCALE_KEY" -H "Scale-Scope: tenant/<id>" \
  -H "content-type: application/json" \
  -d '{ "search": "…" }'

import { client } from "@scale/sdk";
const answer = await client.read.fit_frame({
    "search": "…"
  });
// answer.body is a FitSearchFrame. answer.foot holds the basis, the scope and the time.

import requests
answer = requests.post("https://api.scaleintelligence.co/v1/read/fit_frame",
    headers={"Scale-Key": KEY, "Scale-Scope": "tenant/<id>"},
    json={
      "search": "…"
    }).json()

let answer: Card = client.post("https://api.scaleintelligence.co/v1/read/fit_frame")
    .header("Scale-Key", key).header("Scale-Scope", "tenant/<id>")
    .json(&FitFrameInput { /* the fields of the page */ }).send().await?.json().await?;

MCP tool#

The MCP tool si.read.fit_frame takes the same input and returns the same card. The tool only reads data. An MCP host that renders cards draws this card from ui://scale-intelligence/cards/FitSearchFrame. The tool’s page describes it.

POST /v1/read/fit_frame
Scale-Scope: …
Scale-As-True-On: …
Scale-As-Known-On: …
Scale-Key: …
content-type: application/json

{
  "search": "…"
}