Adzviser MNTN

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---
name: adzviser-data-mode
description: Retrieve and analyze model-visible, row-level MNTN advertising data with Adzviser. Use when the user asks for Data Mode, reporting_mode=data, raw rows, an audit, calculations, comparisons, trends, insights, optimization recommendations, or any answer that requires ChatGPT to inspect the underlying numbers. Do not use when the user explicitly wants only the interactive Preview UI.
---

# Adzviser Data Mode

Use Adzviser as the source of truth for connected MNTN advertising data. Retrieve the actual rows, inspect them, and answer the user's question with evidence from those rows.

## MNTN scope

This plugin supports only connected MNTN accounts. Discover fields with `list_metrics_and_breakdowns_mntn` and use only `assorted_requests.mntn_request`. For another advertising platform or data source, explain the limitation and suggest its corresponding connector.

This is a reporting connector. It cannot create or pause campaigns, change budgets or bids, upload or replace creatives, or publish advertisements. Do not call reporting tools as a substitute for a requested action or claim that a change occurred.

## Route to the data tool

- Call `retrieve_reporting_data` for this workflow.
- Never call `preview_and_retrieve_reporting_data` for Data Mode. That tool is bound to an iframe and will render the embedded report instead of serving as the raw-data path.
- Treat `reporting_mode=data` as user intent that selects `retrieve_reporting_data`; it is not an argument to send.
- Never send `client_id`, `reporting_mode`, or `_widget_action`. They are internal compatibility fields, not model inputs.
- If a user asks for both analysis and an interactive report, retrieve and analyze the rows first. Open Preview UI afterward only if the user explicitly requested that additional deliverable.

## Follow the reporting workflow

1. Interpret the question and identify the requested data sources, measures, dimensions, time window, and comparison periods. Ask one concise question only when a missing choice would materially change the report.
2. Resolve relative dates from the current date available to ChatGPT. Pass inclusive `[start_date, end_date]` pairs in `YYYY-MM-DD` format. Never reuse a fixed date from an example or an older conversation.
3. Call `list_workspace`. Use a user-named workspace when it exists. Otherwise auto-select only when one workspace unambiguously contains every requested connected source; ask when multiple candidates remain.
4. For every requested source, call its `list_metrics_and_breakdowns_*` tool. Use the exact returned field names. Do not guess close variants.
5. Build one `adzviser_request` with:
   - `workspace_name`, except for a documented source that does not require it;
   - `date_ranges` containing every requested period;
   - optional `time_granularity` when the user requests a time series;
   - `assorted_requests` as an object keyed by source request name, `mntn_request` for this plugin.
6. Call `retrieve_reporting_data` once with `{ "adzviser_request": ... }`.
7. Inspect all data sources, date-range segments, headers, rows, and `note_of_assumption` values in `structuredContent.reportingData`. Use the text/CSV content only as a fallback when structured content is absent.
8. Answer with the requested numbers first, then material patterns and practical recommendations supported by the data. State the workspace, sources, and exact date ranges used when they help the user verify the result.

## Compose valid requests

- `assorted_requests` must be an object, never an array.
- Include only requested sources. Do not add empty source requests.
- Each ordinary source request must contain `metrics`; include `breakdowns` only when useful.
- Keep at least one requested metric or breakdown for every included source.
- Do not put Date, Week, Month, Quarter, or Year in `breakdowns`; use `time_granularity` instead.
- Use multiple date-range pairs in one call when the user asks for several periods, including overlapping windows such as the last 30, 14, and 7 days.
- Adzviser does not apply arbitrary report filters. Retrieve the fields needed to identify rows, then filter or calculate from the returned data.

Example shape:

```json
{
  "adzviser_request": {
    "workspace_name": "Workspace Name",
    "date_ranges": [["2026-08-01", "2026-08-20"]],
    "time_granularity": "Date",
    "assorted_requests": {
      "mntn_request": {
        "metrics": ["Total spend", "Impressions", "Total verified visits"],
        "breakdowns": ["Advertiser name"]
      }
    }
  }
}
```

The field labels above illustrate structure only. Always obtain valid labels from the corresponding discovery tool.

## Use MNTN fields correctly

- Use exact discovery labels, including `Total spend`, `Total verified visits`, and `Total conversions`; do not invent generic `Cost`, `Visits`, or `Conversions` labels.
- For advertiser summaries, include `Advertiser name`. For campaign and creative reports, use the corresponding IDs and names. Preserve separate IDs even when names match.
- Do not request contact names, email addresses, phone numbers, order IDs, or other unrelated personal data for ordinary advertising performance reports.
- Keep attributed, site-wide, raw, offline, assist, and last-touch metrics distinct. Do not substitute or add them together as though they represent the same outcome.
- Preserve the returned rate and cost-per-result values. For requested calculations, label the formula, use matching numerator and denominator populations, and guard zero or missing denominators. Do not sum rates, average row-level rates without suitable weighting, or sum reach as though audiences never overlap.
- Monetary values may omit a currency code after normalization. Do not invent a currency or infer spend currency from a conversion-currency breakdown. Returned percent-formatted CSV values retain percentage points when the percent sign is removed; do not multiply such values by 100 again.
- Keep explicit zero metrics, missing values, and attribution assumptions distinct. A period can return visits or conversions alongside zero spend or impressions; report the values without inventing an explanation.
- Different aggregation levels can return different totals. Do not alter rows to force agreement; disclose a discrepancy when comparing them.
- For time series, use only the requested granularity. Provider rate limits can affect reports with many periods. Report a rate-limit failure honestly; do not repeatedly retry, silently change the requested period or granularity, or fabricate a successful report.

## Respect ChatGPT's result boundaries

- `structuredContent` is model-visible and is the preferred basis for analysis. Do not claim the rows are inaccessible when they are present there.
- `_meta` is hidden from the model. Never depend on it for analysis or describe it as returned report data.
- Do not call the preview tool after a successful retrieval merely to display the same request.
- Do not repeat an identical retrieval because a widget or follow-up message appears. Reuse model-visible rows already present in the conversation.
- A successful response with zero rows is a valid no-data result. Report that clearly with the source and date range; do not silently switch modes or invent values.
- If a field or request is rejected, refresh the relevant discovery tool output, correct the request once, and retry. Do not work around schema errors by adding internal parameters.

## Produce a useful analysis

- Distinguish facts in the rows from interpretations and recommendations.
- For audits, check totals and the requested entity-level breakdowns before recommending changes.
- For comparisons, calculate both absolute and percentage changes when denominators are valid; flag zero or missing baselines.
- Preserve units and currency as returned. Do not assume currencies match across accounts or sources.
- Mention assumptions supplied by Adzviser and any material limitations such as missing rows, partial periods, or unavailable fields.

Referenced files: 1 — file references are metadata, not package contents.

View saved version →

---
name: adzviser-preview-ui-mode
description: Open Adzviser's interactive MNTN report preview in ChatGPT so the user can inspect, edit, or run a reporting request in the widget. Use only when the user explicitly asks for Preview UI, UI Mode, an interactive or embedded report, a report builder, or a chance to review or edit the request before running it. Do not use for raw rows, Data Mode, audits, calculations, comparisons, or programmatic analysis.
---

# Adzviser Preview UI Mode

Prepare a valid reporting request and open the Adzviser component once so the user can inspect or edit it interactively inside ChatGPT.

## MNTN scope

This plugin supports only connected MNTN accounts. Discover fields with `list_metrics_and_breakdowns_mntn` and use only `assorted_requests.mntn_request`. For another advertising platform or data source, explain the limitation and suggest its corresponding connector.

This is a reporting connector. It cannot create or pause campaigns, change budgets or bids, upload or replace creatives, or publish advertisements. Do not call reporting tools as a substitute for a requested action or claim that a change occurred.

## Route to the preview tool

- Call `preview_and_retrieve_reporting_data` only for this workflow.
- Do not call it merely because the user says “show me” when the task requires ChatGPT to inspect numbers. Use `retrieve_reporting_data` for analysis, audits, raw rows, calculations, comparisons, and `reporting_mode=data`.
- Treat `reporting_mode=ui` as user intent that selects the preview tool; it is not an argument to send.
- Never send `client_id`, `reporting_mode`, or `_widget_action`. Never call `set_reporting_mode`; that is an app-only preference control used by the component.
- If a user asks for both an analysis and an interactive report, use Data Mode for the analysis and open Preview UI only as a separate, explicitly requested deliverable.

## Follow the preview workflow

1. Identify the requested data sources, measures, dimensions, time window, and comparison periods. Ask one concise question only when a missing choice would materially change the report.
2. Resolve relative dates from the current date available to ChatGPT. Pass inclusive `[start_date, end_date]` pairs in `YYYY-MM-DD` format. Never copy a fixed “today” date from an example.
3. Call `list_workspace`. Use a user-named workspace when it exists. Otherwise auto-select only when one workspace unambiguously contains every requested connected source; ask when multiple candidates remain.
4. For every requested source, call its `list_metrics_and_breakdowns_*` tool and use the exact returned field names.
5. Build one `adzviser_request` with `workspace_name`, `date_ranges`, optional `time_granularity`, and an `assorted_requests` object containing only the requested sources.
6. Call `preview_and_retrieve_reporting_data` exactly once with `{ "adzviser_request": ... }`.
7. Briefly tell the user that the interactive report is ready. Do not invent results or claim that the preview response already contains report rows.

## Compose valid requests

- `assorted_requests` must be an object, never an array.
- Include only requested sources. Do not add empty source requests.
- Each ordinary source request must contain `metrics`; include `breakdowns` only when useful.
- Keep at least one requested metric or breakdown for every included source.
- Do not put Date, Week, Month, Quarter, or Year in `breakdowns`; use `time_granularity` instead.
- Use multiple date-range pairs when the user wants distinct comparison periods.

Example shape:

```json
{
  "adzviser_request": {
    "workspace_name": "Workspace Name",
    "date_ranges": [["2026-08-01", "2026-08-20"]],
    "assorted_requests": {
      "mntn_request": {
        "metrics": ["Total spend", "Impressions", "Total verified visits"],
        "breakdowns": ["Campaign ID", "Campaign name"]
      }
    }
  }
}
```

The field labels above illustrate structure only. Always obtain valid labels from the corresponding discovery tool.

## Use MNTN fields correctly

- Use exact discovery labels, including `Total spend`, `Total verified visits`, and `Total conversions`; do not invent generic `Cost`, `Visits`, or `Conversions` labels.
- For advertiser summaries, include `Advertiser name`. For campaign and creative reports, use the corresponding IDs and names. Preserve separate IDs even when names match.
- Do not request contact names, email addresses, phone numbers, order IDs, or other unrelated personal data for ordinary advertising performance reports.
- Keep attributed, site-wide, raw, offline, assist, and last-touch metrics distinct. Do not substitute or add them together as though they represent the same outcome.
- Preserve the returned rate and cost-per-result values. For requested calculations, label the formula, use matching numerator and denominator populations, and guard zero or missing denominators. Do not sum rates, average row-level rates without suitable weighting, or sum reach as though audiences never overlap.
- Monetary values may omit a currency code after normalization. Do not invent a currency or infer spend currency from a conversion-currency breakdown. Returned percent-formatted CSV values retain percentage points when the percent sign is removed; do not multiply such values by 100 again.
- Keep explicit zero metrics, missing values, and attribution assumptions distinct. A period can return visits or conversions alongside zero spend or impressions; report the values without inventing an explanation.
- Different aggregation levels can return different totals. Do not alter rows to force agreement; disclose a discrepancy when comparing them.
- For time series, use only the requested granularity. Provider rate limits can affect reports with many periods. Report a rate-limit failure honestly; do not repeatedly retry, silently change the requested period or granularity, or fabricate a successful report.

## Respect ChatGPT's component lifecycle

- Calling the preview tool mounts the iframe because the tool descriptor is statically associated with a UI resource. This is why the raw-data path must use a different tool.
- The preview response contains configuration for the component, not the final reporting rows. Do not analyze it as campaign performance.
- The component can call reporting tools through the MCP Apps bridge. Let it handle its own Run and mode-toggle interactions; do not duplicate those calls from the conversation.
- In UI Mode, the rows may stay inside the component for visual inspection. Do not claim to have analyzed rows that were never published to the model.
- If the user switches the component to Data Mode, it can publish model-visible reporting data and send a follow-up message. Analyze those newly supplied rows directly; do not rerun the same report unless the data is absent or the user changes the request.
- `structuredContent` is visible to ChatGPT. `_meta` is component-only and hidden from the model; never rely on `_meta` for conclusions.
- Do not ask the user to confirm or rerun immediately after the preview opens. The component already provides its own controls.

## Handle problems without changing modes

- If the workspace is unavailable, report what `list_workspace` returned and ask the user to choose or connect the needed source.
- Different ChatGPT and advertising-account email addresses do not by themselves prove an authentication problem. Use the connected Adzviser workspace shown by the tools as the authorization context.
- If a metric or breakdown is rejected, refresh the corresponding discovery tool, correct the request once, and retry.
- If the user changes the request to raw data or analysis, stop this workflow and use `retrieve_reporting_data`; do not pass a Data Mode flag to the preview tool.

Referenced files: 1 — file references are metadata, not package contents.