Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
作者提供的说明(英文原文)
Self-hosted MCP server: technical indicators + smart-money-concepts from OHLC bars.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"wickworks": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"docker.io/psyb0t/wickworks:v0.7.0"
]
}
}
}在终端中运行这条命令
claude mcp add wickworks -- docker run -i --rm docker.io/psyb0t/wickworks:v0.7.0粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"wickworks": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"docker.io/psyb0t/wickworks:v0.7.0"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"wickworks": {
"type": "stdio",
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"docker.io/psyb0t/wickworks:v0.7.0"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日
注册名称已通过 GitHub 账户 psyb0t 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(0.7.0)于2个月前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Docker。
同一分类中有:Spec Driven Development, Remote Desktop Commander, FactLot
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
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.
Cited CRE facts and deterministic calculators for agents: rent, CAM, debt sizing, IRR, parcel IDs.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
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.
只显示了开头部分;其余内容请在仓库中阅读