Finseo

Read the actual AI answers for one prompt on one day: the answer text, all brands named in the answer with their sentiment, and every source the model cited or saw. Defaults to the most recent day with answers; pass `date` (YYYY-MM-DD, see get_prompt_details `availableDates`) for a specific day and `model` to narrow to one AI model. `response_format: "concise"` (default) truncates long answer texts to save tokens; use "detailed" for the full text.

get_answer_content

Gap analysis vs one competitor (same numbers as the Gap Analysis tab on the competitor detail page). Returns aggregate counts over distinct (prompt, model, day) answers — `onlyThem` (competitor mentioned, you are not = your losses), `both` (head-to-head), `onlyYou` (your wins) — plus a sample of the concrete prompts behind them. Use `gapType` to choose which prompt list you want: "competitor_only" (default — where you lose), "user_only" (where you win) or "both". Get the `competitorId` from get_competitor_ranking (field `id`).

get_competitor_gap_analysis

Get the competitor visibility ranking for a project: which brands are mentioned most in AI answers over the timeframe. `visibilityRate` here is the share of analyzed AI answers mentioning that brand — the same definition as the dashboard "Visibility" KPI and directly comparable with get_visibility_metrics `visibilityRatePercent`. `avgMentionOrder` is the ordinal "Position" KPI (average rank among all brands named in an answer, 1 = named first — report this when asked about position); `mentionDepthPercent` is how deep in the answer text the mention appears (NOT a rank). Each competitor row includes an `id` — pass it as `competitorId` to get_competitor_gap_analysis or get_competitor_h2h to drill deeper into one competitor. `totalCitations` per competitor is ANSWER-LEVEL: the number of unique AI answers citing that brand's domains in the timeframe — the same unit as the dashboard "Citations" KPI and get_visibility_metrics `ownDomainCitations.total` (which covers only the own brand's exact domain).

get_competitor_ranking

Get per-day visibility for a project over the timeframe — one row per day with visibilityPercent (share of that day's AI answers mentioning the brand; same as the dashboard chart), brand mentions, answers analyzed, avgMentionOrder (ordinal "Position" KPI: average rank among brands named in an answer, 1 = first), mentionDepthPercent (how deep in the answer text the mention appears — NOT a rank) and sentiment. Use this for questions like "how did my visibility change day by day".

get_visibility_timeseries

Head-to-head comparison of your brand vs one competitor over answers that mention BOTH brands: `youFirst`/`themFirst`/`tied` (who is named first, based on ordinal mention position), average mention order for each side, and LLM-judged comparative claims (`claims.you`/`claims.them` = claims favoring each side). Get the `competitorId` from get_competitor_ranking (field `id`).

get_competitor_h2h

Drill into ONE tracked prompt: prompt metadata, aggregated metrics, per-model metrics, and the answer history — one row per AI answer (newest first) with date, model, whether your brand was mentioned, mention depth, sentiment and the brands named in that answer. Get the `promptId` from list_prompts (field `id`). To read the full text of a specific answer, use get_answer_content with the same promptId plus a `date` from `availableDates`.

get_prompt_details

Get the AI query fan-outs (related sub-queries that AI models generated while answering your tracked prompts), aggregated and ranked by frequency. Each fan-out includes total occurrences, distinct prompts that triggered it, models, and last-seen timestamp.

get_query_fanouts

Sentiment and Share-of-Voice overview for a project (same data as the dashboard Sentiment page): uncapped Share of Voice (own mentions vs ALL detected brands, and vs your tracked list — both denominators are explicit), a per-brand sentiment leaderboard (score 0-100, positive/neutral/negative distribution, win rate, strongest/weakest aspect, per-aspect scores) and the most negative LLM-judged statements with their aspect and date.

get_sentiment_overview

Read ONE optimization task in full: description, step-by-step action plan, the evidence (data points and affected prompts/sources) that triggered it, and — for content tasks — a structured content plan (target page, template, prompts to cover). Get the `taskId` from list_tasks (field `id`).

get_task_details

Get the most cited sources in AI responses for a project

get_top_sources