GetMint

Poll content generation progress after trigger_content_generation. Shows current phase, progress %, and any errors.

get_generation_status

Fetch a single tracked topic by ID with its configuration (prompts, LLM models, markets). `name` is the stored label; `displayName` is what the topic is shown as and what its competition prompts compare brands on, and `competitionSubject` overrides that subject when set. Both are absent when unset, in which case `name` is used.

get_topic

Recent competition runs for a topic, newest first: the latest 10 analyses, each with its `reportDate`, YOUR brand `averageScore` (0-100 competition score), `scoreVariation` and `availableModels`. Limits: takes no date and cannot reach analyses older than those 10, so it is not a monthly history; it returns no per-competitor scores. For competitor visibility use get_topic_visibility.

get_topic_competition

Share of voice on a topic: every brand the AI names, tracked or not, ranked by `sharePct` = its share of all brand mentions over the last 14 days, with `shareMargin` and `shareTier` (same tier = too close to call). Tracked brands also carry their visibility `level` and `tier`. Paged with `page`/`pageSize`; `models` takes model ids such as "gpt-interface". Share of voice is always read over the latest 14 days: `startDate`/`endDate` are accepted and do not change it. Always give a score with its range (low–high). Brands in the same `tier` are too close to call: never call one ahead of another inside a tier. When `derived` is true, say the numbers are a combined estimate of the models asked for. Markets are not a filter of this read.

get_topic_mentions

SHOPPING overview of one topic: how often the answers to its prompts came back with product carousels, how often a product of the brand is in them and at what position (its mean slot against the longest carousel), the competitor brands sharing the shelf, the merchants the offers land on and the top products, over a date window, with the topic name. Fed by the prompts of the topic whose modules include shopping, on ChatGPT and Alexa Shopping. This is the SHOPPING surface only: for the answer-engine brand-mention ranking of a topic use `get_topic_mentions`. Empty sections when no prompt of the topic feeds shopping or nothing ran in the window.

get_topic_shopping_overview

Quick headline for a topic, YOUR brand only: `summary.visibility` (the visibility score the page shows: `level` in %, pooled over the last 14 days, its 95% margin `range95`, `verdict` against 14 days earlier, null while nothing is measured), `summary.averageCompetitionScore` (0-100, averaged over the competition analyses of the last 30 days), `lastAnalyzed` and `trends`. Limits: takes no date, model or tag; returns no competitor figures and no sentiment. For competitors and filters use get_topic_visibility.

get_topic_summary

Visibility of a topic, as the visibility page shows it. Returns `own` (your brand: `score` = % of answers naming it over the last 14 days, its 95% range `low`–`high`, `verdict` against 14 days earlier, `tier`), `brands` (tracked brands and the top brands by share of voice, each with `level`, `tier`, `sharePct` = share of all brand mentions, `shareTier`), `trend` (own-brand score on the days that matter), `shifts` (days the AI engine itself changed), `modelsMix`, `status` and `livePrompts`. `startDate`/`endDate` pick the days whose 14-day score is shown, as `trend`: omitted, the last 6 months; a window shorter than 14 days is widened to 14, one longer than 182 days is cut to the latest 182. The score itself is always the latest 14 days. `models` takes the model ids listed in `modelsMix`. Always give a score with its range (low–high). Brands in the same `tier` are too close to call: never call one ahead of another inside a tier. When `derived` is true, say the numbers are a combined estimate of the models asked for. Markets are not a filter of this read.

get_topic_visibility

Visibility of each tracked brand on each AI model of a topic, as the "Brand Visibility by Model" tab shows it: per brand its pooled `level` and `margin`, and `byModel` with each model's `level` and `margin`. A model has fewer answers than the pool, so its margin is wider: two models whose ranges overlap are not different. Filters: `models`, `tagIds`. Always give a score with its range (low–high). Brands in the same `tier` are too close to call: never call one ahead of another inside a tier. When `derived` is true, say the numbers are a combined estimate of the models asked for. Markets are not a filter of this read. When `tooFewPrompts` is true, lead with the warning that fewer than 10 prompts carry the selected tags, so the numbers are not significant, and draw no conclusion.

get_topic_visibility_by_model

Your visibility on each prompt of a topic, as the Prompt Visibility Ranking tab shows it, highest first: `level` (the latest 14-day reading) with its `margin`, `runs` (answers it is built on) and `ratePct` (share of those answers naming you). Use it to find the prompts where you are absent or strong. Filters: `models`, `tagIds` (the on-screen ones by default). One prompt has few answers, so its margin is wide: never compare two prompts whose ranges overlap. Always give a score with its range (low–high). Brands in the same `tier` are too close to call: never call one ahead of another inside a tier. When `derived` is true, say the numbers are a combined estimate of the models asked for. Markets are not a filter of this read. When `tooFewPrompts` is true, lead with the warning that fewer than 10 prompts carry the selected tags, so the numbers are not significant, and draw no conclusion.

get_topic_visibility_prompts

List content briefs for a domain with pagination. Use this as the starting point to discover briefIds before calling other tools.

list_briefs

The prompt tags of a domain, each `{ id, name }`. Use it to turn a tag the user names into the `tagIds` the visibility tools take.

list_domain_tags

List every domain accessible to the authenticated organization. Typically the first call an agent makes — all other insights tools require a domainId.

list_domains

Paginated list of INDIVIDUAL citations — the exact source URLs LLMs cite, item by item (not just the aggregated domains from get_domain_explorer_aggregated). Each row carries `link` (the exact cited URL), `linkDomain`, the analysis type, and the model. Use the filters (the array filters take a list of strings) to drill from a domain to its exact pages: pass `topicName: ["US - GEO - Transactionnel"]` to scope to a topic (use the exact name from list_topics), `linkDomain: ["reddit.com"]` to list the exact Reddit threads, `linkDomain: ["youtube.com"]` for the exact videos, or `linkDomain: ["g2.com"]` to find the exact page to target for a mention. `searchQuery` substring-matches the PROMPT/QUERY text that produced the citation (not the cited URL — use `linkDomain` for that). HARD LIMIT: results are always scoped to the LAST 1 MONTH (the citation set is huge; a wider window would time out). To read further, pass the response's `nextCursor` back as `cursor` (unsorted calls only, same filters); `page`/`pageSize` also work.

list_explorer_citations

List every tracked topic attached to a domain.

list_topics

Enqueue article generation for a brief whose document has been finalized. Consumes CONTENT_GENERATION credits (scaled by word count). Returns immediately (202); poll with get_generation_status. Pass an idempotencyKey and reuse it when you retry a call that timed out or failed: the retry then returns the first call's result instead of generating and charging a second time. If the brief's metadata.brandKitId is set, generation applies the kit's tone, pronoun, style flags, author persona, and blocklist (blocked words/competitors are rewritten out during the polish step).

trigger_content_generation