Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Descripción del creador (original en inglés)
Self-hosted MCP server: technical indicators + smart-money-concepts from OHLC bars.
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"wickworks": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"docker.io/psyb0t/wickworks:v0.7.0"
]
}
}
}Ejecuta esto en tu terminal
claude mcp add wickworks -- docker run -i --rm docker.io/psyb0t/wickworks:v0.7.0Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"wickworks": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"docker.io/psyb0t/wickworks:v0.7.0"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"wickworks": {
"type": "stdio",
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"docker.io/psyb0t/wickworks:v0.7.0"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 6 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub psyb0t, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez –, y la última versión (0.7.0) llegó al registro hace 2 meses.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Docker en tu computadora.
En la misma categoría: Spec Driven Development, Remote Desktop Commander, HotCodePush
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
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.
Manage over-the-air (OTA) updates for React Native, Expo, Capacitor and Cordova apps.
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
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.
Solo se muestra el principio; lee el resto en el repo