---
title: "shine_ask"
description: "Ask a question in natural language, get a grounded answer with inline citations from your SHINE AI organization's indexed data."
---

> Documentation Index
> Fetch the complete documentation index at: https://docs.shineai.io/llms.txt
> Use this file to discover all available pages before exploring further.

# shine_ask

## What it does

Hybrid retrieval + reranking + LLM synthesis. Returns a complete answer with inline `[N]` citations and the source chunks they map to.

## Required scope

`answers:read`

## Input

| Field | Type | Required | Default | Description |
|---|---|---|---|---|
| `query` | string | yes | — | The question, in natural language |
| `scope` | enum | no | `"all"` | `"all"`, `"files"`, `"meetings"`, `"calls"` |
| `timeRange` | enum | no | `"any"` | `"any"`, `"today"`, `"week"`, `"month"` |

## Output

```json
{
  "answer": "SHINE AI maintains SOC 2 Type II attestation covering Security, Availability, and Confidentiality [1]. The auditor is Prescient Assurance LLP [1].",
  "citations": [
    {
      "fileId": "de2e3b54-388b-47d1-a27a-445af0a7df02",
      "fileName": "soc2-controls.md",
      "chunkIndex": 0,
      "snippet": "SHINE AI maintains a SOC 2 Type II attestation..."
    }
  ],
  "usage": {
    "tokens_in": 1842,
    "tokens_out": 196
  }
}
```

## Example invocation

A user asks:

> What did Brian quote Trimark?

The AI app calls `shine_ask({ query: "What did Brian quote Trimark?" })` and gets back a grounded answer with citations to the pricing-conversations file.

## Latency

Typical: 2-6 seconds end-to-end. LLM synthesis dominates (~2-5s). Retrieval itself is ~500ms.

## Notes

- The answer is retrieval-augmented. If the corpus doesn't cover the question, the answer will honestly say so rather than hallucinate.
- Citations are stable — the AI app can present them as source links back to the SHINE web UI.

Source: https://docs.shineai.io/mcp/tools/shine_ask/index.mdx