# Taste Finder (ListeningMind)

> Give a keyword (category, product or brand) and it extracts sensory and emotional words from related searches, SERPs, AI Overviews and People Also Ask, groups them into taste tribes and saves a report. Product planners and marketers use it to choose product language and messages. Data comes from ListeningMind DaaS and Maistro lmpipe for Korea, the US and Japan. Reports can be compared.

Korean: 사람들은 이것을 어떤 감각과 감정의 말로 고르는가(검색 데이터 기반 소비자 취향 리포트) · Japanese: 人はこれをどんな感覚と感情の言葉で選ぶか(検索データに基づく消費者の嗜好レポート)

## Use it as an agent
- [MCP server](https://daas-taste.listeningmind.ai/mcp): Streamable HTTP, JSON-RPC. 20 app tools plus app_guide, result store tools (results_sync, results_list, results_get, results_compare, results_export) and settings sweeps (plan_batch, batch_status)
- WebMCP: every page of this app registers the same tools in `document.modelContext` for a browser agent; calls that spend credits ask the person on the page
- [OpenAPI](https://daas-taste.listeningmind.ai/.well-known/openapi.json): the same tools as a REST contract (operationId = tool name)
- [agents.md](https://daas-taste.listeningmind.ai/agents.md): step-by-step guide · [API catalog](https://daas-taste.listeningmind.ai/.well-known/api-catalog) · [MCP server card](https://daas-taste.listeningmind.ai/.well-known/mcp/server-card.json)
- Every page answers `Accept: text/markdown` with a markdown version
- Tools of kind run or delete spend credits or delete data: over MCP the call returns confirm_url for a person, then call confirmation_status
- Accounts: reading results and history is open. Starting a run or changing data needs a ListeningMind member: the person approving confirm_url signs in or [signs up](https://daas-taste.listeningmind.ai/_lm/auth/signup); other changes over MCP need the member's agent key (LM-User-Key header, created at https://daas-taste.listeningmind.ai/_lm/account)

## What you give and what you get
- Inputs: Keyword (required), market (kr/us/jp), language (ko/en/ja). Seed topics chosen in step 1 (up to 4) and extra seeds typed in, and whether to collect the search journey (extra credits).
- Outputs: HTML taste report (taste tribes, sensory and emotional vocabulary, market facts, evidence sentences, search journey if chosen), downloadable HTML, comparison report of 2-4 reports.
- Cost: From the app docs (unit prices measured 2026-09): one analysis is roughly a few hundred Data API credits (keyword info 10, about 30 per SERP, keyword plus seeds plus 4-12 second-hop queries) plus one Maistro lmpipe analysis (price depends on contract). The search journey option adds 330 or more. Step 1 seed candidates cost little (about 1 credit per candidate). Time: step 1 takes 20-60 seconds, analysis about a minute (longer with much data). Viewing, language switch, comparison and lexicon curation cost 0.

## How a result is made (in order)
1. `list_reports`: Check for an existing report of the same keyword first.
2. `get_input_hint`: Check the keyword's domain and similar saved reports instantly.
3. `get_meaning_hint`: If the word may have several meanings, check its meaning and direction.
4. `explore_seeds`: Step 1: get seed topic candidates and choose them.
5. `analyze_taste`: Step 2: run the analysis after the person confirms (spends credits).
6. `get_report`: Read the report, in another language if needed.
7. `compare_reports`: Compare tastes across reports.
8. `download_report`: Get an HTML file for sharing.
9. `archive_reports`: Star reports to keep.
10. `delete_reports`: Delete reports no longer needed.

## Method (how the analysis works)
1. Step 1: collect search topics strongly related to the keyword, with volume and class, and choose up to 4 seeds.
2. Step 2: collect search data, SERPs, AI Overviews and People Also Ask for the keyword and seeds, widening second-hop queries automatically when results are thin.
3. Extract sensory and emotional words using the shared lexicon.
4. Group consumers into taste tribes, add market facts and evidence sentences, and save the report.
5. Optionally compare 2-4 saved reports.

## Reading the result
- TRIBES is the core: each taste tribe has a name, what it wants and avoids, female and male share, age and evidence.
- The sensory and emotional vocabulary is the language people use when choosing; use it for product language and messages.
- Evidence sentences and market facts show where each tribe comes from; Japan and the US have no demographic data.
- A comparison report of 2-4 reports shows how tastes differ between keywords or markets.

## Requests this app answers well
- Which sensory and emotional words do people use when choosing whisky
- Split coffee bean buyers in the US into taste tribes and list what each wants and avoids
- Compare the perfume taste reports for Korea and Japan
- Find consumer language for our new product's message in search data

## Continue in another app
- [Brand Naming Agent](https://daas-naming.listeningmind.ai/llms.txt) (MCP `https://daas-naming.listeningmind.ai/mcp`): When you are naming a product or brand for this taste. Carry: The top tribe's sensory and emotional words for the description, tone and required word, plus the category keyword.
- [ICSS](https://daas-icss.listeningmind.ai/llms.txt) (MCP `https://daas-icss.listeningmind.ai/mcp`): When you want to test messages built from these words with a synthetic panel. Carry: The keyword and market, and survey options written from each tribe's wants and avoids.

## Using the screens (browser agents)
- `describe_screen`: menus, tabs, fields and buttons of the current screen, and what the page is for
- `go_to`: open a menu or page of this app by its label or path
- `fill_field`: type a value into a field by its label (never presses run; runs go through the run tools, which ask the person)
- `read_this_page`: the text and ids of what the person is looking at

## Tools
- `get_input_hint` (read, GET /hint): Instant advice for a keyword before analysis, from the lexicon, domain detection and saved reports only (no external calls): matching domain and taste cards, similar saved reports, and whether a meaning check is worthwhile (resolve flag). Returns JSON {items, resolve, base}.
- `get_meaning_hint` (run, GET /hint): Second-stage hint: explains what the keyword means, which domain cards it is read with, which directions to analyze (this product only, brand line, whole category) and which other meanings to filter out. Uses the CEP Finder meaning resolver (one LLM call, cached one day per keyword and market; the same call the analysis makes later).
- `explore_seeds` (run, GET /explore): Step 1 of the UI: collects up to 26 strongly related search topics for the keyword with volume, class and flags (meme, AI Overview, no demographic data), pre-checks the recommended ones, and returns an HTML page with that seed set. Read the checked candidates (inputs named s) to pass as s to analyze_taste. Spends a small number of Data API credits.
- `analyze_taste` (run, GET /analyze): Step 2: collects search data, SERPs and AI Overviews for the keyword and up to 4 seeds, extracts sensory and emotional vocabulary, groups consumers into taste tribes and saves a report. Runs synchronously and answers with a redirect to the new report /r/{id}; the HTML report is returned. Spends Data API credits and one Maistro lmpipe analysis call; ask the person before calling.
- `get_report` (read, GET /r/{run_id}): Returns a saved taste report (HTML): taste tribes, sensory vocabulary, emotions, market facts and evidence. lang re-renders it in another language at no cost. For a comparison report, base and who switch the counting view.
- `download_report` (read, GET /r/{run_id}/download): Returns the report as an HTML file attachment.
- `list_reports` (read, GET /): Returns the home page HTML, which contains the report history (keyword, seeds, market, time, archived flag, links /r/{id}). By default it shows the market of the page language; hist=all shows all markets. There is no JSON list endpoint. The landing page no longer lists anyone's runs; use list_history for the signed-in member's own runs.
- `list_history` (read, GET /history): The signed-in member's own runs (taste analyses and comparison reports) across every market, newest first, as an HTML page with links to each report. Sign-in required; other members' runs are not shown.
- `compare_reports` (action, GET /compare): Creates and saves a comparison report of 2 to 4 saved full reports (each must have its model saved; very old reports cannot be compared) and returns it. No credits.
- `archive_reports` (action, POST /archive): Marks or unmarks reports as archived (starred). Archived reports always stay visible in the history regardless of the list limit. Returns {ok, n}.
- `delete_reports` (delete, POST /delete): Permanently deletes up to 50 reports and their saved models. Returns {ok, removed} where removed counts deleted files.
- `get_lexicon` (read, GET /lexicon): Returns the lexicon page: sensory and emotion vocabulary per domain and auto-learning status of candidate words.
- `get_quality_review` (read, GET /quality/review): Curator page listing candidate sensory words of a domain with examples and counts; each row carries tok, dom, lc and ns needed by review_word.
- `get_quality_audit` (read, GET /quality/audit): Curator page to check whether sampled evidence sentences were assigned correctly; each row carries the key needed by mark_evidence.
- `get_quality_cards` (read, GET /quality/cards): Curator page with generated taste cards of a domain and the recompute button; each row carries dom and fid needed by decide_card.
- `review_word` (action, POST /quality/word): Approves, rejects or marks as situation word (ctx, kept out of the lexicon) one candidate word. Changes the shared lexicon used by all future reports. Returns {ok}.
- `mark_evidence` (action, POST /quality/mark): Records whether one sampled evidence assignment is correct, used for the precision figure on the quality dashboard. Returns {ok}.
- `decide_card` (action, POST /quality/card): Approves, rejects or saves edits of a generated taste card (name and layer). Changes shared cards used by reports. Returns {ok}.
- `recompute_reports` (action, POST /quality/recompute): Recomputes saved reports from their stored raw data with the current lexicon and cards, without new data calls: up to 50 reports of a domain, or up to 50 stale reports when dom is omitted. Overwrites those reports. Returns {ok, done, total}.
- `get_report_robots` (read, GET /_lm/report-robots/{run_id}): Returns whether search engines may index this report page (index or noindex), and why: a company member's run or one set by hand is index; a customer's run, and any run that is still going, failed or came back empty, is noindex. Read only; no credits.

## Pages
- [Taste Finder home](https://daas-taste.listeningmind.ai/): Start page: keyword input, input hints and report history (compare, archive, delete).
- [About](https://daas-taste.listeningmind.ai/about): How the analysis works and where data comes from.
- [Sensory lexicon](https://daas-taste.listeningmind.ai/lexicon): Sensory and emotion words per domain and auto-learning status.
- [Lexicon quality](https://daas-taste.listeningmind.ai/quality): Curator dashboard for word review, evidence audit, card review and recompute.

## Limits
- Markets kr, us, jp; languages ko, en, ja. The analysis is one synchronous GET that can take over a minute, beyond the 50-second wait; if it times out, find the new /r/{id} with list_reports a little later. Duplicate runs of the same keyword are not blocked, so check history first. Japan and the US have no demographic data. There is no JSON list API; find reports via /r/{id} links in the home HTML. Quality tools change the shared lexicon used by every report; use them only when a curator asks. No authentication.

## The ListeningMind apps (same account, same tools, call any of them)
Each app has `/mcp`, `/llms.txt`, `/agents.md` and WebMCP on its pages. `family_apps` and `recommend_app` return this list with addresses.
- [ACO](https://daas-aco.listeningmind.ai/llms.txt): Diagnoses whether a product detail page can be cited by AI answers and used by shopping agents (MCP `https://daas-aco.listeningmind.ai/mcp`)
- [AOO](https://daas-aoo.listeningmind.ai/llms.txt): Measures whether AI agents can get real tasks done on a website (MCP `https://daas-aoo.listeningmind.ai/mcp`)
- [Brand Finder](https://daas-brand.listeningmind.ai/llms.txt): Which brands lead search by generation or field, and what their brand assets are (MCP `https://daas-brand.listeningmind.ai/mcp`)
- [Brand Naming Agent](https://daas-naming.listeningmind.ai/llms.txt): Creates and checks brand name candidates grounded in search data (MCP `https://daas-naming.listeningmind.ai/mcp`)
- [Brand Wiki](https://daas-wiki.listeningmind.ai/llms.txt): Turns a brand website into the entities, wiki pages and evidence AI assistants read (MCP `https://daas-wiki.listeningmind.ai/mcp`)
- [CEP Finder](https://daas-cep.listeningmind.ai/llms.txt): In which situations do people start looking for this category (category entry point map) (MCP `https://daas-cep.listeningmind.ai/mcp`)
- [Creative Finder](https://daas-creative.listeningmind.ai/llms.txt): Finds purchase situations (CEP) in search data and drafts a creative, an image and a video for each (MCP `https://daas-creative.listeningmind.ai/mcp`)
- [Demand Forecast](https://daas-forecast.listeningmind.ai/llms.txt): What category demand looks like in the next 3-6 months and when to launch or campaign (MCP `https://daas-forecast.listeningmind.ai/mcp`)
- [Funnel Leak Finder](https://daas-funnel.listeningmind.ai/llms.txt): Where category search journeys leak to competitors (MCP `https://daas-funnel.listeningmind.ai/mcp`)
- [GEO Audit](https://daas-geo.listeningmind.ai/llms.txt): Checks whether a web page is ready to be read and cited by search engines and AI answer engines (MCP `https://daas-geo.listeningmind.ai/mcp`)
- [ICSS](https://daas-icss.listeningmind.ai/llms.txt): Synthetic consumer panel conditioned on search intent, for surveys and FGDs (MCP `https://daas-icss.listeningmind.ai/mcp`)
- [Intent KG](https://daas-intent.listeningmind.ai/llms.txt): What people look for about a topic, why, and how they choose (Intent Knowledge Graph) (MCP `https://daas-intent.listeningmind.ai/mcp`)
- [Prompt Builder](https://daas-prompt.listeningmind.ai/llms.txt): Finds what consumers ask AI and builds managed brand prompts with AI visibility (MCP `https://daas-prompt.listeningmind.ai/mcp`)
- [Pulse Finder](https://daas-pulse.listeningmind.ai/llms.txt): What is rising in search for a gender and age segment right now (MCP `https://daas-pulse.listeningmind.ai/mcp`)
- [Smart Store Suite](https://daas-store.listeningmind.ai/llms.txt): Which Smart Store products to grow, and how to rewrite their titles and detail pages in searchers' words (MCP `https://daas-store.listeningmind.ai/mcp`)
- [ListeningMind Data API](https://daas.listeningmind.ai/llms.txt): the search data behind every app (MCP `https://daas.listeningmind.ai/mcp`) · [glossary](https://daas.listeningmind.ai/en/glossary) · [all apps](https://daas.listeningmind.ai/en/apps)

## Optional
- ListeningMind data and other apps: https://www.listeningmind.com · contact m-support@listeningmind.com