mcp-scentrev

Perfume MCP with a sense of smell Free fragrance MCP and perfume MCP server for AI agents.

CommunityOnlineNo sign-inGlobalFreeRead-only

What it can do

  • Search Fragrances: Search the fragrance catalog by name, brand, or slug prefix. Returns public_id, fragrance_slug, brand, and ratio-safe rating metadata. Use at least 2 characters; returns up to 10 ro
  • Search Fragrances Filtered: Advanced catalog search: notes, accords, brand, gender, season, wear, rating, appreciation. Always returns at most 10 results (result_limit capped at 10). Use result_offset
  • Search By References: Multi-reference note blending: find fragrances whose pyramid overlaps two or more reference scents (Layering Lab style). Always returns at most 10 rows. WHEN TO USE - "mix X and

What data it sees

Do you need an account

No: the server works without sign-in

Perfume MCP with a sense of smell Free fragrance MCP and perfume MCP server for AI agents. Query 126k+ scents for longevity, sillage, seasonality, and community scores from Cursor or Claude

How to connect

How to connect mcp-scentrev to Claude

The server address to paste into Claude settings:

https://mcp-scentrev--kandimallashashankreddy-7rf1.run.tools

  1. Open Claude (claude.ai or the desktop app).
  2. Go to Settings → Connectors.
  3. Click Add custom connector.
  4. Paste the server address copied below into Remote MCP server URL and click Add.
  5. In a chat, click + → Connectors and switch the new connector on.

Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

Authorization

After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

How to connect mcp-scentrev to ChatGPT

The server address to paste into ChatGPT settings:

https://mcp-scentrev--kandimallashashankreddy-7rf1.run.tools

  1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
  2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
  3. Open Settings → Connectors and click Create.
  4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
  5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
  6. In a new chat open + → Apps/Connectors and enable the connector.

OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

Authorization

On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

How to connect mcp-scentrev to Cursor

The server address to paste into Cursor settings:

https://mcp-scentrev--kandimallashashankreddy-7rf1.run.tools

Fastest: click Open in Cursor below and confirm the prompt.

Manually:

  1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
  2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
  3. Save the file. The server appears in the MCP list; authorize it there if asked.

Authorization

If the service needs sign-in, an authorize button appears next to the server in the MCP list.

Server tool list (22)

Raw names from tools/list. Only developers need these.

search_fragrancesSearch the fragrance catalog by name, brand, or slug prefix. Returns public_id, fragrance_slug, brand, and ratio-safe rating metadata. Use at least 2 characters; returns up to 10 rows.
search_fragrances_filteredAdvanced catalog search: notes, accords, brand, gender, season, wear, rating, appreciation. Always returns at most 10 results (result_limit capped at 10). Use result_offset to page. WHEN TO USE - Note/accord/brand/season discovery → this tool (not search_fragrances). - Name/brand prefix lookup → search_fragrances only. SEASON — filter_season: spring|summer|fall|winter. If min_season_category is omitted, the server defaults it to good to avoid timeouts on broad season browse. BRAND — scope results to one or more houses (names auto-slugified): - filter_brand_slug — single brand, e.g. "Tom Ford", "lattafa", "afnan" - filter_brand_slugs — multiple brands (OR), e.g. ["afnan", "tom ford"] - Omit brand to search the high-confidence slice (vote floor + capped pool). Pass a brand when the question is about one house. NOTES (substring match; iris matches Orris Root) - top_notes / middle_notes / base_notes — layer-specific; ALL patterns required per layer. - notes — any pyramid layer when layer not specified. ACCORDS — all patterns must match (substring). GENDER — filter_gender: male|female|unisex FILTERS — min_*_category (exact label) OR min_*_score (0-1): - min_longevity_category, min_sillage_category, min_projection_category - min_season_category (requires filter_season; auto-default good) - min_rating_category, min_appreciation_category - min_rating_votes (default 200 server-side; pass 0 to disable), min_reviews_count - max_rating_category, max_reviews_count — underrated / hidden-gem discovery SORT (descending): rating (default) | reviews | engagement | discovery | appreciation | longevity | sillage | projection | season. PAGINATION — result_offset (default 0). When truncated=true, request offset+limit next. DEBUG — explain=true echoes filters_applied; applied_defaults lists server injections. After search, use get_fragrance_profile(sections=[...]) or granular get_* tools for detail.
search_by_referencesMulti-reference note blending: find fragrances whose pyramid overlaps two or more reference scents (Layering Lab style). Always returns at most 10 rows. WHEN TO USE - "mix X and Y", "blend Le Beau with Virgin Island Water" → search_by_references (NOT get_reminds_of — that is single-fragrance community votes). - Manual note list without references → search_fragrances_filtered(notes=[...]). REFERENCE SLUGS — reference_slugs: 2+ slugs from search_fragrances. - note_mode: weighted (default) | intersect | union - max_notes_per_ref (default 5) samples across top/middle/base (not top-only) - min_note_overlap_ratio (default 0.20) - exclude_references: omit reference fragrances from results (default true) FILTERS & SORT — same as search_fragrances_filtered; sort_by default overlap. Candidates are capped to high-vote fragrances so famous pairs (Aventus, Sauvage) return instead of timing out. Supports result_offset and explain like filtered search. OUTPUT — search row plus overlap_score, overlap_ratio, blend metadata, matched_notes.
list_brandsBrowse catalog brands with fragrance counts. Optional query filters by brand name or slug substring. Returns up to 10 brands.
resolve_slugFuzzy-resolve a slug, fragrance name, or brand fragment to catalog entries with match_type (exact_slug, slug_prefix, name, brand). Use before get_fragrance_profile when the user gives an informal name.
compare_fragrancesSide-by-side wear metrics, rating, appreciation, and price_value for two or more fragrances. Pass fragrance_slugs and/or public_ids.
get_fragrance_profileFetch a ratio-safe profile for one fragrance. Defaults to the identity section. Pass sections for performance, notes, accords, or price. Provide public_id or fragrance_slug from search results.
get_wear_summaryWear-focused subset: identifiers plus longevity, sillage, projection, season fit, and time_of_day. No notes, perfumers, or pros/cons. Provide public_id or fragrance_slug.
get_identityFetch identity fields: description excerpt, gender, and star rating. Rating score is 0-1 (community stars are 0-5). Provide one identifier.
get_performanceFetch wear metrics: longevity, sillage, projection, season fit, and time of day. All scores are 0-1 with category labels.
get_appreciationFetch community sentiment on a hate-to-love scale with ratio-safe scores.
get_reminds_ofList fragrances that smell similar. Only like_ratio is exposed — no raw votes.
get_note_pyramidReturn top, heart, and base note names for one fragrance.
get_accordsReturn dominant accords with percentage weights (0-100, not 0-1).
get_scent_cardOne-call decision card: name, rating, longevity, sillage, projection, best season, appreciation, top accords, and three similar scents. Prefer this over calling get_identity, get_performance, get_appreciation, and get_accords separately.
suggest_occasionShortlist up to 5 scents for a named occasion. occasion: office | date_night | summer_day | winter_evening | fresh | cozy | signature | sport. Optional filter_gender: male | female | unisex. Uses a fixed accord filter so the search stays fast.
find_dupesFind cheaper or adjacent alternatives using reminds_of, price_value, and note overlap.
blind_buy_riskComposite blind-buy risk from appreciation variance and rating reliability.
decode_tasteAnalyze loved fragrances to surface shared note/accord DNA and recommendations.
wardrobe_addAdd a fragrance to the authenticated user's wardrobe.
wardrobe_listList the authenticated user's wardrobe items.
wardrobe_analyseReport coverage gaps in the user's fragrance collection.