patternfetch

One API/MCP call turns a crypto ticker + timeframe into a token-compact market-state brief — so an LLM trading agent never has to dump raw OHLCV into its context.

Instead of hundreds of raw candles per ticker, you get: compact candles + a SAX shape signature, detected chart/candlestick patterns (double top/bottom, head & shoulders, engulfing, hammer, doji), support/resistance clusters, a regime label, and interpreted indicators (RSI / EMA / ATR) — plus a one-line summary. Crypto-first. Impersonal and non-directive by design — data, never buy/sell advice.

Why

Feeding raw OHLCV into the model context is huge, slow, and the model re-derives the same structure every single call. patternfetch does that work server-side and hands back a brief that's a fraction of the tokens.

Endpoints

MCP config (remote, streamable-http)

{
  "mcpServers": {
    "patternfetch": {
      "url": "https://patternfetch.com/mcp",
      "headers": { "Authorization": "Bearer YOUR_PATTERNFETCH_KEY" }
    }
  }
}
Get a key: curl -X POST https://patternfetch.com/v1/keys -H "Content-Type: application/json" -d '{"email":"you@example.com"}'

Example

curl -X POST https://patternfetch.com/v1/brief \
  -H "Authorization: Bearer <key>" -H "Content-Type: application/json" \
  -d '{"ticker":"BTC","timeframe":"1h"}'

Payment

Pay per call via x402 (USDC on Base, no account) or Stripe (cards). Every response includes an exact cost block.

- Website: https://patternfetch.com
- Not investment advice. Impersonal market data only.

**Schritt für Schritt im Formular:**
1. Name → `patternfetch`
2. Title → obige Zeile
3. Type → `server`
4. Tags → obige Liste
5. Avatar URL → `https://github.com/MarvinRey7879.png`
6. Server config → die Remote-JSON von vorhin
7. Content → obiger Markdown-Block
8. Absenden.