How do I install Patternfetch?

Copy the http config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Patternfetch need an API key?

No required environment variable is listed on this record.

What transport does Patternfetch use?

Patternfetch is reached over HTTP.

When was Patternfetch last checked?

Last checked 2026-10-16. US stocks, ETFs, crypto → compact brief: patterns, S/R, regime + base rates vs baseline. Not advice.

Patternfetch MCP server

patternfetch

patternfetch is a market-data API for AI agents covering US stocks, ETFs and crypto spot. One call with a ticker and a timeframe returns a token-compact market-state report: compact candles, detected chart and candlestick patterns, support and resistance levels, market regime, and interpreted indicators (RSI, EMA). Every detected pattern carries its backtested historical hit rate and its lift against the pattern-free baseline of the same market, so an agent can tell a pattern that carries information from one that does not. Six tools — brief, multi, delta, analogs, scan, capabilities — reachable over REST and MCP, with one-click OAuth, credit billing via Stripe or x402 USDC on Base, a keyless demo endpoint, and $3 starter credit on signup. Impersonal market data, not investment advice.

Why it's smaller: for BTC/USDT 4h (120 candles), a raw OHLCV dump is ~3,260 tokens of just numbers the model still has to analyze; patternfetch's interpreted analysis is ~1,323 tokens, already decided. Reproduce it (no account needed).

Coverage: US stocks and ETFs (split- and dividend-adjusted, delayed/EOD, via Yahoo), crypto spot (realtime, via Binance).
Timeframes: 1m, 5m, 15m, 30m, 1h, 4h, 1d, 1w.
Access: REST at patternfetch.com/v1/*, MCP at patternfetch.com/mcp (Streamable HTTP), plus a local stdio bridge (patternfetch-mcp).

Why base rates and lift

A detector that only reports double_top, confidence 0.92 tells an agent nothing about whether that pattern has ever meant anything. patternfetch attaches an evidence block to each detected pattern:

{
  "name": "double_top",
  "confidence": 0.92,
  "evidence": {
    "scope": "US stocks & ETFs",
    "tf": "1d",
    "band": "0.75-1.00",
    "horizon": 10,
    "n": 7508,
    "hitRate": 0.431,
    "ci95": 0.011,
    "lift": {
      "baseline": 0.419979,
      "baselineN": 46038,
      "lift": 0.011021,
      "ci95": 0.012075,
      "informative": false,
      "reading": "indistinguishable-from-baseline"
    }
  }
}

hitRate is the realizable gross directional base rate: the fraction of non-overlapping historical occurrences of that pattern, in that timeframe and confidence band, whose close-to-close return over the next horizon bars went the expected direction. The forward window starts at detection, so there is no lookahead. No stops, fees or slippage are modelled.

lift compares that hit rate against the baseline of the same market with no pattern present. Many patterns come back indistinguishable-from-baseline — that is the honest result, and reporting it is the point. An agent can filter on informative instead of trusting a geometric confidence score.

Calibration. Across 105 audited categories, 3 fall outside their confidence interval — fewer than the ~5.3 that chance alone predicts across 105 comparisons. For US stocks it is 0 of 60. Method and full tables: patternfetch.com/pattern-base-rates-study. The measurement is reproducible with the open-source honest-signals tool.

Quickstart

No key required — the demo endpoint is public:

curl -X POST https://patternfetch.com/v1/demo \
  -H 'content-type: application/json' \
  -d '{"ticker":"AAPL","timeframe":"1d"}'

With a key (self-serve, $3 starter credit):

curl -X POST https://patternfetch.com/v1/keys -d '{"email":"[email protected]"}'

curl -X POST https://patternfetch.com/v1/brief \
  -H 'authorization: Bearer pf_...' \
  -H 'content-type: application/json' \
  -d '{"ticker":"BTC/USDT","timeframe":"4h"}'

JavaScript client:

npm install patternfetch

import { Patternfetch } from 'patternfetch';

const { key } = await new Patternfetch().createKey('[email protected]');
const pf = new Patternfetch({ apiKey: key });

const brief = await pf.brief({ ticker: 'AAPL', timeframe: '1d' });

console.log(brief.analysis.nl);
// "AAPL: uptrend (strong), +0.14% last 1d, RSI 71.66 (overbought),
//  bearish_engulfing (conf 1, hist 41% over 10b, lift -0.7pp vs 42% base (within noise))."

for (const p of brief.analysis.patterns) {
  if (p.evidence?.lift.informative) console.log(p.name, p.evidence.hitRate, p.evidence.lift.lift);
}

Tools

Six tools, the same set over MCP (patternfetch_*) and REST (POST /v1/*).

Tool	What it returns	When an agent calls it
`brief`	Market-state report for one ticker + timeframe: compact candles, patterns with base rate and lift, support/resistance, regime, RSI/EMA, one-line summary.	The default. It needs the current technical picture of one market without dumping raw OHLCV into context.
`multi`	One brief per timeframe (default `1h`, `4h`, `1d`) plus a cross-timeframe alignment read that spells out agreement or divergence, e.g. `1h up / 4h up / 1d down`.	It wants to know whether a setup is confirmed or contradicted across horizons, without three separate `brief` calls.
`delta`	Only what changed since the last brief for that ticker + timeframe — trend flips, new patterns, RSI-state changes. Returns `changed: false` when nothing material moved.	It polls the same market repeatedly. Call `brief` once, then `delta` on every later poll to keep token cost near zero.
`analogs`	Historical windows whose shape resembles current price action, with the full distribution of what followed: win rate, median, mean, min, max and n over a fixed forward horizon.	It wants the historical outcome spread for a setup rather than a point estimate. Not a prediction, not a strategy backtest.
`scan`	Screener over a curated universe of liquid US large-caps, core and sector ETFs and major crypto pairs. Filter by asset class, regime, pattern and minimum base rate; rows return ranked by base rate with 95% CI. Precomputed daily.	It needs to find candidates across the market rather than analyse a ticker it already named. Feed the shortlist into `brief`.
`capabilities`	Supported assets, timeframes, endpoints, limits and pricing. No input.	First, before relying on any assumption about coverage.

Client methods

Method	Endpoint
`brief({ticker, timeframe, limit?, fields?, market?})`	`POST /v1/brief`
`multi({ticker, timeframes?, limit?, market?})`	`POST /v1/multi`
`delta({ticker, timeframe, limit?})`	`POST /v1/delta`
`analogs({ticker, timeframe, window?, horizon?})`	`POST /v1/analogs`
`scan({assetClass?, regime?, pattern?, tf?, minBaseRate?, limit?})`	`POST /v1/scan`
`candles({ticker, timeframe})`	`POST /v1/candles`
`platforms()`	`GET /v1/platforms`
`createKey(email)`	`POST /v1/keys`

MCP

patternfetch is a remote MCP server (Streamable HTTP) at https://patternfetch.com/mcp. Tools: patternfetch_brief, patternfetch_multi, patternfetch_delta, patternfetch_analogs, patternfetch_scan, patternfetch_capabilities. Discovery (initialize, tools/list) is free — no key. Only tools/call needs auth.

One-click OAuth (nothing to paste) — in Claude Code, Claude Desktop, Cursor or Smithery, add the URL and authorize once; a free-tier key is minted for you:

claude mcp add --transport http patternfetch https://patternfetch.com/mcp

In claude.ai: Customize → Connectors → Add custom connector → https://patternfetch.com/mcp → Authorize.

Or with a Bearer key — add to your MCP config:

{
  "mcpServers": {
    "patternfetch": {
      "url": "https://patternfetch.com/mcp",
      "headers": { "Authorization": "Bearer pf_..." }
    }
  }
}

Get a free key (small starter credit) at https://patternfetch.com/v1/keys.

Local stdio bridge

Prefer a local stdio server (Claude Desktop, sandboxes, no inbound HTTP)? This package ships patternfetch-mcp, a zero-dependency stdio↔HTTP bridge that exposes the same tools and forwards calls to patternfetch.com:

{
  "mcpServers": {
    "patternfetch": {
      "command": "npx",
      "args": ["-y", "patternfetch-mcp"],
      "env": { "PATTERNFETCH_API_KEY": "pf_..." }
    }
  }
}

tools/list works with no key and falls back to the embedded snapshot (mcp-tools.json) when the remote is unreachable, so introspection always succeeds. Tool calls use PATTERNFETCH_API_KEY, OAuth or x402. Override the endpoint with PATTERNFETCH_MCP_URL.

Refresh the snapshot from the live server:

curl -s -X POST https://patternfetch.com/mcp \
  -H 'content-type: application/json' \
  -H 'accept: application/json, text/event-stream' \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list"}'

Pricing

$3 starter credit on signup, at least $0.50 of it usable immediately without a card. After that, pay per call from credit, topped up via Stripe or x402 USDC on Base. Studio plan: $19/month including $25 of usage.

Call	Price
`/v1/brief`	$0.010
`/v1/multi`	$0.025
`/v1/delta`	$0.008 ($0.001 when nothing changed)
`/v1/candles`	$0.005
`/v1/analogs`	$0.050
`/v1/scan`	$0.020

Live figures: GET /v1/platforms.

Legal

patternfetch provides impersonal market data and algorithmic signals for informational purposes only. NOT investment, financial, legal or tax advice, and not a recommendation to buy, sell or hold any security or crypto-asset. Outputs are not personalized to you. Base rates are gross directional frequencies without stops, fees or slippage; past performance and historical analogs do not guarantee future results. Markets are volatile — you may lose all capital. Do your own research. See patternfetch.com/disclaimer, /methodology and /terms.

patternfetch

Why it's smaller: for BTC/USDT 4h (120 candles), a raw OHLCV dump is ~3,260 tokens of just numbers the model still has to analyze; patternfetch's interpreted analysis is ~1,323 tokens, already decided. Reproduce it (no account needed).

Coverage: US stocks and ETFs (split- and dividend-adjusted, delayed/EOD, via Yahoo), crypto spot (realtime, via Binance).
Timeframes: 1m, 5m, 15m, 30m, 1h, 4h, 1d, 1w.
Access: REST at patternfetch.com/v1/*, MCP at patternfetch.com/mcp (Streamable HTTP), plus a local stdio bridge (patternfetch-mcp).

Why base rates and lift

{
  "name": "double_top",
  "confidence": 0.92,
  "evidence": {
    "scope": "US stocks & ETFs",
    "tf": "1d",
    "band": "0.75-1.00",
    "horizon": 10,
    "n": 7508,
    "hitRate": 0.431,
    "ci95": 0.011,
    "lift": {
      "baseline": 0.419979,
      "baselineN": 46038,
      "lift": 0.011021,
      "ci95": 0.012075,
      "informative": false,
      "reading": "indistinguishable-from-baseline"
    }
  }
}

Quickstart

No key required — the demo endpoint is public:

curl -X POST https://patternfetch.com/v1/demo \
  -H 'content-type: application/json' \
  -d '{"ticker":"AAPL","timeframe":"1d"}'

With a key (self-serve, $3 starter credit):

curl -X POST https://patternfetch.com/v1/keys -d '{"email":"[email protected]"}'

curl -X POST https://patternfetch.com/v1/brief \
  -H 'authorization: Bearer pf_...' \
  -H 'content-type: application/json' \
  -d '{"ticker":"BTC/USDT","timeframe":"4h"}'

JavaScript client:

npm install patternfetch

import { Patternfetch } from 'patternfetch';

const { key } = await new Patternfetch().createKey('[email protected]');
const pf = new Patternfetch({ apiKey: key });

const brief = await pf.brief({ ticker: 'AAPL', timeframe: '1d' });

console.log(brief.analysis.nl);
// "AAPL: uptrend (strong), +0.14% last 1d, RSI 71.66 (overbought),
//  bearish_engulfing (conf 1, hist 41% over 10b, lift -0.7pp vs 42% base (within noise))."

for (const p of brief.analysis.patterns) {
  if (p.evidence?.lift.informative) console.log(p.name, p.evidence.hitRate, p.evidence.lift.lift);
}

Tools

Six tools, the same set over MCP (patternfetch_*) and REST (POST /v1/*).

Tool	What it returns	When an agent calls it
`brief`	Market-state report for one ticker + timeframe: compact candles, patterns with base rate and lift, support/resistance, regime, RSI/EMA, one-line summary.	The default. It needs the current technical picture of one market without dumping raw OHLCV into context.
`multi`	One brief per timeframe (default `1h`, `4h`, `1d`) plus a cross-timeframe alignment read that spells out agreement or divergence, e.g. `1h up / 4h up / 1d down`.	It wants to know whether a setup is confirmed or contradicted across horizons, without three separate `brief` calls.
`delta`	Only what changed since the last brief for that ticker + timeframe — trend flips, new patterns, RSI-state changes. Returns `changed: false` when nothing material moved.	It polls the same market repeatedly. Call `brief` once, then `delta` on every later poll to keep token cost near zero.
`analogs`	Historical windows whose shape resembles current price action, with the full distribution of what followed: win rate, median, mean, min, max and n over a fixed forward horizon.	It wants the historical outcome spread for a setup rather than a point estimate. Not a prediction, not a strategy backtest.
`scan`	Screener over a curated universe of liquid US large-caps, core and sector ETFs and major crypto pairs. Filter by asset class, regime, pattern and minimum base rate; rows return ranked by base rate with 95% CI. Precomputed daily.	It needs to find candidates across the market rather than analyse a ticker it already named. Feed the shortlist into `brief`.
`capabilities`	Supported assets, timeframes, endpoints, limits and pricing. No input.	First, before relying on any assumption about coverage.

Client methods

Method	Endpoint
`brief({ticker, timeframe, limit?, fields?, market?})`	`POST /v1/brief`
`multi({ticker, timeframes?, limit?, market?})`	`POST /v1/multi`
`delta({ticker, timeframe, limit?})`	`POST /v1/delta`
`analogs({ticker, timeframe, window?, horizon?})`	`POST /v1/analogs`
`scan({assetClass?, regime?, pattern?, tf?, minBaseRate?, limit?})`	`POST /v1/scan`
`candles({ticker, timeframe})`	`POST /v1/candles`
`platforms()`	`GET /v1/platforms`
`createKey(email)`	`POST /v1/keys`

MCP

One-click OAuth (nothing to paste) — in Claude Code, Claude Desktop, Cursor or Smithery, add the URL and authorize once; a free-tier key is minted for you:

claude mcp add --transport http patternfetch https://patternfetch.com/mcp

In claude.ai: Customize → Connectors → Add custom connector → https://patternfetch.com/mcp → Authorize.

Or with a Bearer key — add to your MCP config:

{
  "mcpServers": {
    "patternfetch": {
      "url": "https://patternfetch.com/mcp",
      "headers": { "Authorization": "Bearer pf_..." }
    }
  }
}

Get a free key (small starter credit) at https://patternfetch.com/v1/keys.

Local stdio bridge

{
  "mcpServers": {
    "patternfetch": {
      "command": "npx",
      "args": ["-y", "patternfetch-mcp"],
      "env": { "PATTERNFETCH_API_KEY": "pf_..." }
    }
  }
}

Refresh the snapshot from the live server:

curl -s -X POST https://patternfetch.com/mcp \
  -H 'content-type: application/json' \
  -H 'accept: application/json, text/event-stream' \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list"}'

Pricing

Call	Price
`/v1/brief`	$0.010
`/v1/multi`	$0.025
`/v1/delta`	$0.008 ($0.001 when nothing changed)
`/v1/candles`	$0.005
`/v1/analogs`	$0.050
`/v1/scan`	$0.020

Live figures: GET /v1/platforms.

Patternfetch

Add this server

Host a copy with our keys and logs.

Related MCP

Related skills

patternfetch

Why base rates and lift

Quickstart

Tools

Client methods

MCP

Local stdio bridge

Pricing

Legal

Patternfetch

Add this server

Host a copy with our keys and logs.

Related MCP

Related skills

patternfetch

Why base rates and lift

Quickstart

Tools

Client methods

MCP

Local stdio bridge

Pricing

Legal