Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
作者による説明(英語の原文)
Self-hosted MCP server: technical indicators + smart-money-concepts from OHLC bars.
この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。
このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。
使っているAIアプリを選んで、設定をコピーしてください。
必要なもの
どのアプリを使っていますか?
上でアプリを選んでください。選んだアプリを覚えておくので、次からはボタンに「…にインストール」と表示されます。
Claude Desktop → Settings → Developer → Edit Config を開き、claude_desktop_config.json にこれを貼り付けます
{
"mcpServers": {
"wickworks": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"docker.io/psyb0t/wickworks:v0.7.0"
]
}
}
}ターミナルでこれを実行します
claude mcp add wickworks -- docker run -i --rm docker.io/psyb0t/wickworks:v0.7.0プロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"mcpServers": {
"wickworks": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"docker.io/psyb0t/wickworks:v0.7.0"
]
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
プロジェクトの .vscode/mcp.json に貼り付けます
{
"servers": {
"wickworks": {
"type": "stdio",
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"docker.io/psyb0t/wickworks:v0.7.0"
]
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
これはパソコンで動くサーバーですが、ChatGPTがつなげるのはインターネット上のサーバーだけです。ほかのアプリを選んでください。
選んだアプリは、この端末のCookieに保存されます。アカウントは不要です。
この設定は、作者のレジストリ登録情報から作成しています。READMEと違う場合は、READMEに従ってください。
パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。
誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月7日にチェック
レジストリ名は、リポジトリを所有するGitHubアカウント psyb0t で確認済みです
このリポジトリはまだGitHubから読み取っていません
このリポジトリはまだGitHubから読み取っていません
これはパソコンで動くため、インターネット上のエンドポイントはありません
パソコンで動き、代わりにローカルの設定を使います
パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです
レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。
当てはまる基本チェック1項目のうち1項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。
レジストリには必須のキーは載っていません。ただし、最初に使うときにサインインを求めるサーバーもあります。
あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません(サーバー自体がオンラインサービスを呼び出す場合を除きます)。
GitHubリポジトリの最終更新は–、最新バージョン(0.7.0)がレジストリに届いたのは2 か月前です。
Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。パソコンにDockerも必要です。
同じカテゴリには:Spec Driven Development, Remote Desktop Commander, FactLot
同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。
カードの見方:★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日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.
作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。
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.
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