Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
제작자가 쓴 설명(영어 원문)
Self-hosted MCP server: technical indicators + smart-money-concepts from OHLC bars.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"wickworks": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"docker.io/psyb0t/wickworks:v0.7.0"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add wickworks -- docker run -i --rm docker.io/psyb0t/wickworks:v0.7.0프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"wickworks": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"docker.io/psyb0t/wickworks:v0.7.0"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"wickworks": {
"type": "stdio",
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"docker.io/psyb0t/wickworks:v0.7.0"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 6일 점검
레지스트리 이름이 GitHub 계정 psyb0t 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.7.0)은 2개월 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Docker도 있어야 해요.
같은 카테고리에는: Spec Driven Development, Remote Desktop Commander, topo^alpin
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Avalanche bulletins and mountain weather forecasts for any place in the Alps.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
The dumb-as-rocks OHLC analyzer. You throw bars at it over HTTP, it throws indicators and SMC objects back. That's the whole product. No database, no queue, no state, no opinions, no "AI-powered signals," no upsell to a $97/mo Discord. Just primitives.
Every snake-oil-flavored TA SaaS out there wants to tell you when to buy. Wickworks tells you the order block is at 1.0832 and the RSI is 71.4. The "what does that mean?" part is where your strategy lives — and it should live in your code, not behind someone else's paywall.
Built on pandas_ta and smartmoneyconcepts, wrapped in a FastAPI server, locked behind 370 tests that diff our output against closed-form references on real EURUSD ticks. If a math bug slips in, the test suite screams before the container builds.
GET /healthGET /metadataPOST / — computeindicators object| Category | Primitives |
|---|---|
| Trend | SMA/EMA + 15 other moving averages, slope, Donchian channels, Ichimoku |
| Momentum | RSI, MACD, Stochastic, StochRSI, ADX, MFI, CCI, Williams %R, ROC, MOM, TSI, TRIX, UO, Fisher |
| Volatility | ATR, NATR, Bollinger Bands, Keltner Channels, Squeeze |
| Volume | VWAP (anchored), VWMA, OBV, AD, ADOSC, CMF, KVO |
| SMC | Order Blocks, Fair Value Gaps, BOS/CHoCH, swing structure, S/R levels, liquidity, retracements, sessions, previous-period H/L |
| Summaries | Position, slope, momentum, volume regime, recent range — pre-baked projections over the raw series |
All JSON field names are camelCase. Output is NaN-safe — NaN becomes null, never a literal NaN token that blows up downstream parsers. NumPy/Pandas scalars and arrays are serialized cleanly (no numpy.float64(...) leaks). Bars in UTC, math in UTC, container runs TZ=UTC — timezone bullshit is your problem, not ours.
docker run --rm -p 8000:8000 psyb0t/wickworks:latest
That's it. The service listens on :8000.
services:
wickworks: image: psyb0t/wickworks:latest ports: ["8000:8000"] environment: LOG_LEVEL: INFO MAX_BARS: "5000" MIN_BARS: "50"
make run # build the dev image and run uvicorn on :8000
Three endpoints. That's the whole surface.
GET /healthcurl -s http://localhost:8000/health
{ "ok": true, "version": "0.7.1" }
GET /metadataReturns the versioned catalog of labels, descriptions, units, categories, and interpretation notes for every output path. Consumers can cache it for the reported Wickworks version.
POST / — computeSend OHLC(V) bars + the indicators you want. Get back only what you asked for — response keys mirror the keys you sent. No "let me also throw in 40 indicators you didn't ask for" energy.
curl -s -X POST http://localhost:8000/ \
-H 'Content-Type: application/json' \ -d '{ "symbol": "EURUSD", "timeframe": "H1", "bars": [ { "time": 1700000000, "open": 1.0832, "high": 1.0851, "low": 1.0828, "close": 1.0844, "volume": 1247 }, ... ], "indicators": { "rsi": true, "rsi21": { "type": "rsi", "length": 21 }, "stochFast": { "type": "stoch", "k": 5, "d": 3, "smoothK": 3 }, "stochSlow": { "type": "stoch", "k": 21, "d": 7, "smoothK": 5 }, "macd": true, "orderBlocks": true, "fvg": true } }'
indicators object — the whole pointEach entry maps an output name (the key) to a spec:
true — run the indicator with default params; key doubles as the type.{ ...params } — params object; missing type falls back to the key.{ "type": " ", ...params } — run a known indicator under a custom output name. This is how you stack multiple instances of the same indicator (e.g. four stochs with different params, three EMAs at different lengths)."indicators": {
"rsi": true, "rsi21": { "type": "rsi", "length": 21 }, "stochA": { "type": "stoch", "k": 5, "d": 3 }, "stochB": { "type": "stoch", "k": 21, "d": 7 } }
The response contains rsi, rsi21, stochA, stochB. Nothing else. Duplicate output names are physically impossible by JSON-object construction — you can't shoot yourself in the foot with this API even if you try.
The outputs below map to a handful of recurring trading ideas. If you've used any TradingView-style charting tool most of these will be familiar; if not, the short framing here is enough to pick the right output for the job.
Series vs events. A _Series_ output is one value per input bar (warmup positions are null) — these are continuous quantities you can chart. An _event_ output is a sparse array of objects pinpointing things that just happened (a swing, a block, a structure break). Series tell you _state_; events tell you _occurrences_.
Primitives only — no signals. Wickworks does not emit interpretive signals (no divergence detection, no MA-cross events, no "buy/sell" tags). Everything returned is either a raw indicator series, a structural fact (an order block was formed at this bar, price closed past this swing), or a pre-baked summary over those — never a judgment about what to do. If you want divergences, MACD-cross events, golden/death crosses, or any other derived signal, build that layer in your own consumer.
The four questions every indicator answers part of:
In-house event constructs you won't find on TradingView:
Want the formal contract for tooling / validators? See
schema.json— full JSON Schema Draft 2020-12. The reference below is the human version: categories + per-indicator blurbs + params tables + return shapes + examples.
Three series shapes are shared across most outputs:
number | null), aligned 1:1 with input bars. Warmup positions are null.0/1 integer per bar.-1/+1 integer per bar (1 = bullish/long, -1 = bearish/short).Indicators below are grouped by what they tell a trader, not by parameter shape. Each subsection starts with a one-paragraph framing of the category, then lists every indicator in it with a short "what it is / when to use it" blurb.
Smoothed price lines. Each flavor trades responsiveness against lag differently — pick by how fast you want the curve to react to new bars. Trader use: define the dominant direction (price above/below the MA = bull/bear bias), identify dynamic support/resistance the market keeps touching, fire crossover signals (fast MA crossing slow MA = trend shift).
All take a single length parameter and return a Series.
type | default | inputs | what it is |
|---|---|---|---|
ema | 21 | close | Exponential MA — recent bars weighted more. Standard trend filter. The default trend MA in most strategies. |
sma | 50 | close | Simple MA — flat average. Slow, smooth, classic. 50/200-SMA crosses define the "Golden Cross" / "Death Cross". |
hma | 14 | close | Hull MA — low-lag, smooth. Reacts fast without the noise an EMA would give at the same period. |
wma | 14 | close | Weighted MA — linear weights. Sits between SMA and EMA in lag. |
dema | 10 | close | Double-EMA — less lag than EMA via a correction term. |
tema | 10 | close | Triple-EMA — even less lag, but more whipsaw-prone in chop. |
t3 | 10 | close | Tillson T3 — smooth like SMA, fast like EMA. Curve-looking output some traders prefer for visual clarity. |
kama | 10 | close | Kaufman Adaptive MA — speeds up in trends, slows down in chop. Self-tuning. |
alma | 10 | close | Arnaud Legoux MA — Gaussian-weighted. Low noise, low lag tradeoff. |
linreg | 14 | close | Linear-regression MA — best-fit line over the window, evaluated at "now". Statistically grounded smoothing. |
jma | 7 | close | Jurik MA — proprietary smooth, very low lag. Premium-feeling curve. |
zlma | 10 | close | Zero-Lag MA — error-correction on EMA, attempting to remove lag entirely. |
rma | 10 | close | Wilder's smoothing — used inside RSI/ATR. Heavy, slow. Useful when you want indicator-internal smoothing semantics. |
| fwma | 10 | close | Fibonacci-weighted MA — weights by Fib sequence.
앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요