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

# Calls & Interviews

> 6 MCP tools for accessing and managing completed interview calls — transcripts, analysis, and usage stats.

Calls are completed interview sessions. Each call record carries the transcript, recording URL, AI analysis, and quality signals. 6 tools.

| Tool                   | Description                                                              |
| ---------------------- | ------------------------------------------------------------------------ |
| `list_calls`           | List interview calls, filterable by study/participant/status/quality     |
| `get_call`             | Fetch one call (includes transcript, messages, recording\_url, analysis) |
| `update_call`          | Update mutable fields (e.g. `is_visible`, `success_evaluation`)          |
| `delete_call`          | Soft-delete a call                                                       |
| `get_call_usage_stats` | Aggregate call counts/durations over a date range                        |
| `generate_report`      | Trigger study-level report generation                                    |

## Response shaping

**Response shaping.** `get_call` and `list_calls` return lean summaries by default:

* `view` — `"summary"` (default) or `"full"` (raw record).
* `include` — array of heavy fields to expand, e.g. `["transcript"]`, `["messages"]`, `["screener_questions"]`.
* `fields` — exact allowlist of top-level fields (`id` always returned); overrides `view`/`include`.

By default large screener option lists are truncated and transcripts are returned as a short excerpt. Pass `include` or `view: "full"` to fetch the omitted data.

Default `page_size` is 5 for `list_calls` (max 100). Using `include` on a list multiplies payload by page\_size — prefer `get_call` for full heavy fields on a single record.

## How User Intuition handles this

Every interview User Intuition runs is stored as a searchable, analyzable call record. `generate_report` triggers cross-study synthesis — the same engine powering the in-app dashboard reports. See [`/platform/agentic-research/`](https://www.userintuition.ai/platform/agentic-research/) for the full agent-driven research workflow.

## AI participant detection

Each scored call is checked for whether the **participant** (not the AI moderator) was likely an AI or bot rather than a genuine human — for example, someone relaying the questions through a chatbot and reading the answers back. The result is stored on the call's `success_evaluation_details` JSON under the `ai_participant_check` key:

```json theme={null}
{
  "ai_participant_check": {
    "ai_participant_suspected": true,
    "confidence": 0.9,
    "signals": ["AI self-disclosure", "essay-like enumerated answers"],
    "reasoning": "Short explanation of why the call was flagged."
  }
}
```

This check is **flag-only**: it never changes the call's `success_evaluation` score. A flagged call is surfaced to admins for review and can be moderated (excluded from results via `is_visible`, have its pending panel reward voided, or be re-scored). `confidence` is a 0–1 suspicion score and is `0.0` when not suspected; the detector is biased toward false negatives so genuine, articulate participants are not flagged.