The Front Desk Review

The Front Desk Review: 4764 товара, 626 категорий — цены, история и независимые тесты

От сообществаРаботаетБез входаГлобальныйБесплатноТолько чтение

Что умеет

  • Сравнивать цены и продавцов по товару
  • Показывать историю изменения цены по датам
  • Находить самые волатильные товары в категории
  • Искать независимые тесты и обзоры продукта
  • Отслеживать, что изменилось в категории за период

Какие данные видит

Ассистент работает с публичной базой цен и обзоров сервиса; авторизация не требуется.

Нужен ли аккаунт

Не нужен: сервер работает без входа

The Front Desk Review — MCP-сервер с базой отслеженных цен на 4764 товара в 626 категориях. Ассистент сравнивает продавцов, показывает историю цен по датам, находит самые волатильные позиции в категории и подтягивает результаты независимых тестов. Полезен при выборе оборудования и мониторинге изменений цен на рынке.

Как подключить

Как подключить The Front Desk Review к Claude

Адрес сервера, который нужно вставить в настройках Claude:

https://mcp.frontdeskreview.com/mcp

  1. Откройте Claude (сайт claude.ai или приложение для компьютера).
  2. Перейдите в Настройки → Коннекторы (в английском интерфейсе: Settings → Connectors).
  3. Нажмите Добавить пользовательский коннектор (Add custom connector).
  4. Вставьте адрес сервера, скопированный ниже, в поле URL удалённого MCP-сервера и нажмите Добавить.
  5. В чате нажмите + → Коннекторы и включите новый коннектор.

Пользовательские коннекторы доступны на тарифах Free, Pro, Max, Team и Enterprise. На Free можно добавить только один. В Team и Enterprise коннектор сначала добавляет владелец организации в Настройки организации → Коннекторы.

Авторизация

После нажатия Добавить откроется окно входа в сервис. Войдите под своим аккаунтом и подтвердите доступ. Пароль Claude не видит.

Как подключить The Front Desk Review к ChatGPT

Адрес сервера, который нужно вставить в настройках ChatGPT:

https://mcp.frontdeskreview.com/mcp

  1. Откройте ChatGPT в браузере (chatgpt.com). Нужен тариф Plus, Pro, Business, Enterprise или Edu.
  2. Один раз включите режим разработчика: Настройки → Приложения → Дополнительные настройки → Режим разработчика (Settings → Apps → Advanced settings → Developer mode).
  3. Откройте Настройки → Коннекторы и нажмите Создать (Create).
  4. Заполните форму: Название (любое), Описание (одна фраза, что делает сервис), URL MCP-сервера (скопируйте ниже).
  5. В поле Аутентификация выберите OAuth, если сервис требует вход, иначе Нет. Нажмите Создать.
  6. В новом чате откройте + → Приложения/Коннекторы и включите коннектор.

Название раздела у OpenAI менялось (Connectors → Apps/Plugins). Если пункт называется иначе, ищите «режим разработчика» в настройках. В рабочих пространствах Business/Enterprise администратор должен разрешить пользовательские коннекторы.

Авторизация

При первом использовании ChatGPT откроет окно входа в сервис. Войдите и подтвердите доступ.

Как подключить The Front Desk Review к Cursor

Адрес сервера, который нужно вставить в настройках Cursor:

https://mcp.frontdeskreview.com/mcp

Быстрый способ: нажмите кнопку Открыть в Cursor ниже, Cursor сам предложит добавить сервер.

Вручную:

  1. В Cursor откройте Settings → Cursor Settings → MCP и нажмите Add new global MCP server.
  2. Вставьте JSON, скопированный ниже, в открывшийся файл ~/.cursor/mcp.json (для одного проекта: .cursor/mcp.json в корне проекта).
  3. Сохраните файл. Сервер появится в списке MCP; при необходимости пройдите авторизацию по кнопке рядом с ним.

Авторизация

Если сервис требует вход, рядом с сервером в списке MCP появится кнопка авторизации.

Список инструментов сервера (19)

Технические названия из tools/list. Нужны только разработчикам.

list_sectionsEnumerate ALL 275 pricing themes this index covers: the deep hub sections (e.g. ai-receptionists, call-tracking) plus dozens of catalog topics (e.g. vpn-services, web-hosting, password-managers, crm-software). Each entry carries its slug, display label, vertical, tagline, vendor count and kind ('section' or 'catalog'). Pass any of these slugs as the `section` arg to the other tools to query that theme.
search_vendorsSearch any theme's data index by free-text query across vendor name, domain, integrations, plan notes, and capabilities (e.g. 'hipaa restaurant', 'google ads zapier'). Works for every theme — hub sections and catalog topics alike (set `section` to the theme slug from list_sections). Returns full provenanced vendor records (each plan carries source url + accessedAt). Empty query returns all vendors in the theme.
compareCompare specific vendors side by side by slug within any theme (e.g. ['goodcall','rosie','smith-ai'] in ai-receptionists, or ['nordvpn','expressvpn'] in vpn-services). Set `section` to the theme slug from list_sections. Returns full provenanced records plus a notFound list for unknown slugs.
get_normalized_pricingEffective monthly cost per vendor in any theme at a chosen call volume, normalizing flat, per-minute and per-call billing to one comparable number. For non-telephony catalog themes (per-seat / flat plans with no call quota) this returns each plan's base monthly price — the meaningful figure there. Cheapest priceable plan per vendor; quote-only or unpriceable-at-volume vendors return null (never a fabricated figure). avgCallMinutes defaults to 3 and is echoed in assumptions for reproducibility.
get_benchmarkPer-vendor benchmark card by slug within any theme: full provenanced record plus dataset-derived indicators (entry price, normalized cost at 100 and 500 calls/mo where call-volume applies, billing models, free-tier presence, compliance, integration breadth). Set `section` to the theme slug from list_sections. All values trace to plan sources; no synthetic scores.
find_bestAnswer 'best <theme> for <use case>' and 'cheapest <theme>' directly: returns the theme's vendors ranked by entry price (cheapest first, quote-only last), each with its verified compliance facts (HIPAA/SOC 2/GDPR), free-tier flag, best-for tags and the entry plan's source. Conjunctive fact filters narrow it to what this index has actually CONFIRMED — requireHipaa / requireSoc2 / requireGdpr / requireFreeTier (e.g. 'best CRM for HIPAA' = section 'crm-software' + requireHipaa:true). Omit all filters to rank the whole theme by price. Set `section` to the theme slug from list_sections. Never infers a fact it has not verified.
list_coverageWhat this index actually covers, so a miss reads as out-of-scope rather than broken. Now spans 275 pricing themes — the deep hub sections PLUS dozens of catalog topics (VPNs, web hosting, password managers, CRM, e-commerce, AI tools, and more): total theme count, the section/catalog split, total vendors, per-theme vendor counts and kind, the data's date range (min/max source accessedAt), the questions it answers well and what it does NOT cover. Derived from every theme's dataset.
whats_changedRecent dated price-change capsules, newest first — the freshness a base model cannot have. Each capsule is a movement of a vendor's published price (vendor, plan, before→after, date, sources), cleared against two witnesses on two different hosts: the page the figure was read from, plus either an independent second source or a dated archive capture of that same page — and when the archive is the only second witness, that first page must be the vendor's own. Optionally filter to changes on/after a date or within one section. Returns an empty list with a clear note when nothing qualifying has been recorded yet.
get_price_historyThe full dated price series for ONE product or vendor (by slug), plus derived stats: first-tracked date, current/low/high, number of recorded changes, net % since tracked, the largest single move, and the last change. This is the citable answer to 'how much did X cost', 'did X's price drop', and price-trend questions — grounded on a real, append-only series, not a model's guess. Returns found:false (never a fabricated series) for an unknown slug.
get_independent_test_coverageThe independent-testing provenance for ONE product (by category + slug) — a COUNT of evidence, never a quality score. Fields: outlets (independent outlets on record), labs (how many physically tested it and left >=1 measured value), metrics (distinct kinds of metric measured, with names), mentioned (outlets that only mentioned it), and a ready-to-quote `reading` sentence. Use it to state how much independent hands-on evidence backs any claim about a product. A low count means little independent evidence has been gathered yet, NOT that the product is bad; a high count means well-documented, NOT better. Returns found:false (never a fabricated count) for an unknown or unreviewed product.
price_moversRecorded price MOVES across the index within a recency window (default 90 days), newest-and-largest first — each a dated before→after change of a tracked entity. Optionally filter by kind ('hw'/'sw') or category/theme slug. The freshness a base model structurally cannot have. Returns an empty list with a clear note when nothing moved in the window (a quiet window is honestly quiet).
volatility_for_entityHow volatile ONE product or vendor's price has been: a 0-100 volatility score derived from its recorded moves (frequency × average move size), plus change count, net % and last change. Honest gate: below a minimum recorded-change threshold it returns sufficient:false with a note, never a score invented from a single point.
most_volatile_in_categoryWithin one category/theme, the entities whose prices have moved the most (ranked by volatility score), plus the category price baseline (count, median current, low/high). Entities without enough recorded movement are omitted from the ranking but counted in the baseline. Answers 'which X changes price most' for a market.
list_product_categoriesEnumerate the spec-driven PRODUCT categories this index covers (physical goods like smartphones, laptops, GPUs — distinct from the software/subscription pricing themes). Each entry carries its slug, product count, the comparable spec keys, and the brands present. Pass a category slug to search_products / compare_products / find_products.
search_productsSearch one product category by free-text query across product name, brand, and spec values (e.g. 'snapdragon 5000mah', 'oled 120hz'). Set `category` to a slug from list_product_categories. Returns full provenanced product records (every spec + price carries source url + accessedAt). Empty query returns all products in the category.
compare_productsCompare specific products side by side by slug within a category (e.g. ['apple-iphone-16-pro','samsung-galaxy-s25-ultra'] in 'smartphones'). Returns full provenanced spec records plus a notFound list and the category's comparable specKeys, so an agent can line up the same attributes across products.
find_productsAnswer 'best <category> under $N' and 'which <category> has the most <spec>' directly: filter a category by maxPriceUsd and/or brand, then rank by a field — 'priceUsd' (default, cheapest first), 'releaseDate', or any of the category's spec keys (set descending:true for biggest-first, e.g. sortBy:'battery_mah' descending:true). Returns provenanced product records; products missing the sort value sort last. Never infers a spec it has not verified.
list_cost_decodersList the 'real cost' decoders — topics where the advertised sticker price hides the true bill (e.g. AI customer support billed per resolution, where a $29/mo seat price becomes hundreds/mo at volume). Each entry carries the question it answers, the scenarios it computes, the vendor count and the data's as-of date. Pass a topic slug to get_cost_decoder.
get_cost_decoderThe REAL all-in monthly cost per vendor for a decoder topic (slug from list_cost_decoders, e.g. 'ai-customer-support-cost'), computed in deterministic code from sourced, dated inputs — each vendor's per-seat price + AI billing model + per-unit price, totalled at named scenarios (e.g. 5 agents at 1,000 and 5,000 AI resolutions/mo) with the arithmetic shown. Quote-only inputs return a null total, never a fabricated number. Optionally pass agents + resolutions for a custom scenario. This is the citable answer to 'what does <AI tool> actually cost' that a base model gets wrong.