Get started
Build with scentrev-mcp
Ten MCP tools that turn community fragrance knowledge into clean, structured data. Three steps from zero to your first response.
Generate a key
Sign in with Google, GitHub, or email, then create a key on the API Keys page. One active key per account.
Add the config
Paste one block into Cursor or Claude Desktop, or call the REST RPCs directly with your bearer token.
Ask away
Your agent now answers fragrance questions with structured, sourced data — longevity, sillage, sentiment, and more.
Connect a client
Point any MCP client at https://api.scentrev.com/mcp/ and authenticate with your bearer key. The same key works for direct REST calls at https://api.scentrev.com.
{
"mcpServers": {
"fragrance": {
"url": "https://api.scentrev.com/mcp/",
"headers": {
"Authorization": "Bearer frag_live_YOUR_KEY"
}
}
}
}Generate the key from the dashboard after signing in. Creating a new key revokes the previous one.
Output rules
Four invariants every response follows, so you can trust the shape of the data you build on.
Ratios only
Raw likes, dislikes, vote JSON, and 0–5 star averages are never exposed. You get normalized scores instead. Star rating is out of 5 — convert with score × 5, never × 10.
n_records, not confidence
Every metric carries n_records — how many community votes shaped it. Over 500 is a strong signal; under 100 is thin data.
0–1 everywhere
All wear metrics are normalized to 0–1 with a category label. The one exception: accord percentages run 0–100.
Rating vs reviews
Star rating uses n_records (rating votes) on a 0–5 scale. reviews_count is how many written text reviews exist — a separate number, not a /5 or /10 score.
Stable identifiers
Reference fragrances by public_id (UUID) or fragrance_slug. Internal database IDs never appear in responses.
A typical metric object
Score, category, vote count, and how to read the axis — every time.
{
"score": 0.62,
"category": "moderate",
"n_records": 1240,
"scale": "0 = X, 1 = Y"
}