Insights tools
Query brand visibility, sentiment, alignment, citations, and shopping from an LLM agent
The 13 Insights tools answer questions like "how are LLMs describing my brand?" and "which sources cite us?" — all read-only, no credits consumed.
Served at https://mcp.getmint.ai/mcp. See Connect, Authentication, and the Capabilities matrix. The MCP is a curated read set; the full Insights surface (per-report detail, prompt config, topic management, wider shopping, sponsored ads) stays on the REST API.
Discovery
list_domains — Every domain your org can reach. Usually the first call; other tools need a domainId.
Inputs: none.
get_domain — One domain's brand attributes, aliases, tracked markets/languages, owned sources.
Inputs: domainId (required).
list_topics — A domain's tracked topics (competitors live here, not on the domain).
Inputs: domainId (required).
get_topic — One topic's config: prompts, LLM models, markets.
Inputs: domainId, topicId (required).
get_topic_summary — Snapshot analytics for a topic: visibility score, competition leaders, sentiment.
Inputs: domainId, topicId (required).
Visibility & competition
get_topic_visibility — How often LLMs mention your brand vs each competitor. Returns a transformed payload: visibilityScore (your brand, 0–100), scoreVariation (change vs previous period, percentage points), competitors ({ name, visibilityScore }, ranked highest-first), modelBreakdown (per-model), topMentions (top 10).
Inputs: domainId, topicId (required).
Sends no query params, so it always uses the default: last 6 months window. For a custom window, call GET /domains/{id}/topics/{topicId}/visibility/aggregated over REST — see the Cookbook.
get_topic_mentions — Share-of-voice leaderboard: brands ranked by how often LLMs name them. Returns mentions ([{ mention, count, percentage }], by count), mentionsDetailed (per-model { mention, modelId, count }), and pagination.
Inputs: domainId, topicId (required); page, pageSize (optional, max 100).
get_topic_competition — Latest head-to-head competition report across LLM prompts.
Inputs: domainId, topicId (required).
Brand perception
get_domain_sentiment_aggregated — "How are LLMs describing my brand?" — aggregated sentiment scores, quotes, and trend. modelBreakdown[] gives each model its own average score.
Inputs: domainId (required); fromDate, toDate (optional, inclusive ISO dates); citationsPage, citationsPageSize (optional).
get_domain_alignment_aggregated — How closely LLM depictions match the brand's stated attributes. modelBreakdown[] gives each model its own average score; modelAttributeScores splits it by attribute.
Inputs: domainId (required); fromDate, toDate (optional, inclusive ISO dates); citationsPage, citationsPageSize (optional).
Every scan produces one report per model. Both aggregates average each model over its own reports, so modelBreakdown[].score is comparable across models even when one of them ran on fewer days.
fromDate/toDate translate to the endpoint's startDate/endDate; omit them for the last 6 months. These are the only two date-filterable Insights tools.
Both reports carry a citations block, and it is paginated default 20 · max 100. citations.items holds one page; totalItems, totalPages and hasMore describe the rest.
Prop
Type
Inline, the citation list is 93-99% of these responses — hundreds of KB on an active domain, past what some agent hosts accept in one tool result. Ask for the page you need; for the complete list over an arbitrary window, use GET /domains/{id}/{sentiment,alignment}/aggregated over REST.
Citations & sources
get_domain_explorer_aggregated — What your brand is known for in LLM answers. Returns topKeywords and topSources (each { count, percentage }), summary, domainSourceAnalysis, availableModels, availableTopics, reportCount, dateRange, totalAnswers. Top lists are top 25 and webSearchResults is dropped to stay compact.
Inputs: domainId (required); topicId (optional — narrows to one topic; visibility + competition only).
list_explorer_citations — Paginated individual citations, row by row. Each row carries link (cited URL), linkDomain, analysis type, and model. Filter to drill down to exact threads, videos, and pages.
Prop
Type
Always scoped to the last 1 month 1-month cap — the citation set is huge and a wider window would time out. Page with page/pageSize; for arbitrary ranges, query the REST endpoint directly.
Products & places
get_mention_entities — The product lines and places your brands are named through: a specific hotel property, store or branch, rather than the brand as a whole. Returns rows with displayName, productText, countryCode, cityText, localityText, ownership (ourbrand / competitor / other, resolved live against your current configuration), mentions and mentionRate.
mentions counts distinct answers — an answer naming one property three times counts once — and mentionRate is that over every answer in the window, 0-100.
Inputs: domainId (required), plus groupBy (entity by default, or product / country / city / locality / brand), segment, search, sortBy, sortDir, page, limit and the usual window filters.
get_mention_entity_geo — Where those brands are named. Returns two independent layers: countries (per-country totals, always populated) and points (distinct places, with coordinates where they have been resolved). unlocatedPoints says how many places carry no coordinates yet; they still count toward their country total, so countries is never under-reported by them.
Inputs: domainId (required), plus segment, brandName and the usual window filters.
Both require brand entity extraction on the organization. Without it there is simply nothing to read: the tools return an empty list rather than an error.
Shopping
get_top_shopping_brands — Brands ranked by frequency across the domain's shopping/product queries (Amazon, Google Shopping, …). Shopping surface only — for the answer-engine ranking use get_topic_mentions. Empty list when no shopping queries are configured.
Inputs: domainId (required).
Example Agent Session
User: How is my brand positioned vs. competitors for the topic "cloud security"?
Agent:
list_topics→ found "Cloud Security".get_topic_visibility→ your brand 34%, Competitor A 28%, Competitor B 19% (ranked, per-model split).get_topic_mentionsgives the same ranking by raw mention share;list_explorer_citations(linkDomain: ["reddit.com"]) surfaces the threads driving it;get_domain_sentiment_aggregatedconfirms sentiment is positive. Wantget_topic_competitionfor head-to-head win rates?