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Arsenal Decision Engine

作者:Faouzi122版本 2.0.1i★ –i在云端运行
注册名称io.github.Faouzi122/arsenal-decision-engine

安装

概览

作者提供的说明(英文原文)

R_net LP risk evaluator for DeFAI agents. IL + Breakeven Corridor O(1). L402 Lightning paywall.

PopNx MCP 的三大支柱

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。

它能帮你做什么

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。

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

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 …”。

适用于网页版、桌面版和手机版 Claude:

  1. 打开 Settings → Connectors
  2. 点击 Add custom connector
  3. 粘贴下面的地址,然后点击 Add
  4. 如果要求登录,请登录作者的服务
SettingsConnectors1+ Add2https://…3
https://api.arsenal-quant.com/mcp

在终端中运行这条命令

claude mcp add --transport http arsenal-decision-engine https://api.arsenal-quant.com/mcp

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "arsenal-decision-engine": {
      "url": "https://api.arsenal-quant.com/mcp"
    }
  }
}

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。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "arsenal-decision-engine": {
      "type": "http",
      "url": "https://api.arsenal-quant.com/mcp"
    }
  }
}

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。

ChatGPT 只能连接互联网上的服务器:

  1. 打开网页版 ChatGPT → Settings → Connectors (Apps)
  2. 点击 Create 或 Add(部分套餐需要先在 Advanced 中开启 Developer mode)
  3. 粘贴下面的地址,起个名字,然后保存
  4. 如果要求登录,请登录作者的服务
SettingsConnectors1+ Create2https://…3
https://api.arsenal-quant.com/mcp

菜单名称可能因套餐和版本不同而略有差异。

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。

它能做什么

我们还没有读取这个服务器的工具列表;我们会轮流检查各个端点。

基础检查i

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8日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 Faouzi122 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    端点有响应

    尚未检查;我们会轮流检查各个端点

  • … 尚未检查

    使用 OAuth 登录

    尚未检查;我们会轮流检查各个端点

  • … 尚未检查

    工具描述中没有隐藏指令

    尚未检查;我们会在检查端点时读取工具列表

与“其他”中的其他服务器比较i

  • 星标数多于此分类中其他 18433 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 18433 个服务器的 17% 更新
  • 基础检查结果等于或优于此分类中其他 18433 个服务器的 100%

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。

常见问题

Arsenal Decision Engine 安全吗?

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。

它需要哪些密钥或代码?

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。

它在我的电脑上运行还是在云端?

它在作者的云端运行。通过互联网连接,无需安装,但你发送的内容会经过作者的服务器。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(2.0.1)于4个月前进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接,所以接受远程 MCP 服务器的应用也可以使用,例如开启开发者模式的 ChatGPT。

有什么可以替代它?

同一分类中有:Spec Driven Development, Remote Desktop Commander, MyGroceryPal

替代选择

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

Spec Driven Development

作者:formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

在你的电脑上运行★ 440MIT14个月前更新
安装

Remote Desktop Commander

作者:desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

在云端运行★ 2113天前更新
安装

MyGroceryPal

作者:mygrocerypal.co.uk

UK supermarket prices: compare products and baskets across 13 stores, see price history, set alerts.

在云端运行今天更新
安装

作者的 README

打开 README

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。

Arsenal Decision Engine 🛡️

The Risk-Validation Layer for Autonomous AI Agents (DeFAI)

Method and raw results are published — backtest script · result data (180 days of Binance ETH/USDC daily closes):

🔬 Breakeven Corridor is a deterministic algebraic boundary (where IL = accumulated yield). Any position whose price ratio stays within [lower_be, upper_be] has R_net > 0 by mathematical definition — not a probabilistic model.

📐 This engine measures; it does not forecast. No predictive-accuracy figure is claimed — read the published result files and judge the method for yourself.


Mission

Transform DeFi uncertainty into deterministic, actionable risk metrics for autonomous agents. We do not run stateful trading bots or generate speculative prediction signals; we provide a stateless risk middleware layer that agents query before deploying or maintaining standard constant-product / full-range LP positions.

Built for agents. 100 free calls per IP per day — no wallet, no sign-up, custom parameters included. An L402 payment path is implemented in the gateway but is not operational in production: today, the engine is free to use.


What This Engine Does

Before an autonomous agent deploys capital or adjusts a standard constant-product / full-range LP position (such as Uniswap V2 or full-range V3), it submits the pool parameters (APY, price ratio, days held) to our API. The engine computes the exact mathematical risk, the net return ($R_{net}$), and the dynamic Breakeven Corridor bounds.

  • No LLMs. No hallucinations. Pure algebraic calculation.
  • Complexity: $\mathcal{O}(1)$ time and memory.
  • Latency: $< 15\text{ms}$ local execution.
Two ways to call it

1. MCP JSON-RPC — the endpoint advertised on the MCP registry

POST https://api.arsenal-quant.com/mcp
{"jsonrpc":"2.0","id":1,"method":"tools/call",

"params":{"name":"evaluate_pool", "arguments":{"apy":0.20,"price_ratio":0.85,"days_held":30}}}

Standard MCP handshake: `initialize` → `tools/list` → `tools/call`. Available over

streamable HTTP and stdio.

2. REST convenience route — no MCP client required

GET https://api.arsenal-quant.com/mcp/evaluate?apy=0.20&price_ratio=0.85&days_held=30

Both routes run the same calculation and the same quota. Note that /mcp/evaluate is GET-only: a POST to that path returns 405 Allow: GET, because JSON-RPC belongs on /mcp.

Engine Response (JSON Contract)
{

"impermanent_loss_pct": 0.3292, "accumulated_yield_pct": 1.6438, "r_net_pct": 1.3146, "il_to_yield_ratio": 0.2, "risk_level": "LOW", "breakeven_corridor": { "lower_ratio": 0.6941, "upper_ratio": 1.4407, "interpretation": "Position remains profitable if price ratio stays within [0.6941, 1.4407]" }, "inputs": { "apy": 0.2, "price_ratio": 0.85, "days_held": 30 }, "source": "Arsenal Decision Engine v2.0", "oracle_signature": " ", "layer": "FREE" }

`layer` reports how the call was served: `FREE` while inside the free quota,

PREMIUM once an L402 payment has been verified. The call shown above is served as FREE.


Access and Pricing

  • Free tier — `evaluate_pool`: 100 calls per IP per day, custom parameters included. No Lightning wallet is needed. This is the only tier currently in service.
  • Beyond the free quota: the gateway implements the L402 challenge and returns 402 with a WWW-Authenticate header. The payment rail is not operational in production — invoices issued today are not settleable, and no payment is expected or accepted. Treat the paid tier as announced, not available.
  • `GET /mcp/audit/latest`: 3 free calls per IP per hour; beyond that the route returns 402. That response documents the protocol; it is not a live payment path.

Python Integration Example

import urllib.request

import urllib.error import json import re import os

API_URL = "https://api.arsenal-quant.com/mcp/evaluate?apy=0.20&price_ratio=0.85&days_held=30" LNBITS_URL = "https://demo.lnbits.com"

LNbits requires a wallet key with send permission to pay an invoice.

Use a DEDICATED wallet funded with a small working balance, and never the key

of a wallet holding significant funds. Keep it in the environment, never in code.

LNBITS_PAYMENT_KEY = os.getenv("LNBITS_PAYMENT_KEY")

def query_risk_oracle(): req = urllib.request.Request(API_URL, method="GET") req.add_header("x-agent-id", "autonomous-lp-bot")

try: with urllib.request.urlopen(req) as resp: return json.loads(resp.read().decode('utf-8')) except urllib.error.HTTPError as e: if e.code == 402: auth_header = e.headers.get("WWW-Authenticate") macaroon = re.search(r'token="([^"]+)"', auth_header).group(1) invoice = re.search(r'invoice="([^"]+)"', auth_header).group(1)

pay_req = urllib.request.Request( f"{LNBITS_URL}/api/v1/payments", data=json.dumps({"out": True, "bolt11": invoice}).encode(), headers={"X-Api-Key": LNBITS_PAYMENT_KEY, "Content-Type": "application/json"} ) with urllib.request.urlopen(pay_req) as pay_resp: preimage = json.loads(pay_resp.read().decode())["preimage"]

retry_req = urllib.request.Request(API_URL, method="GET") retry_req.add_header("Authorization", f"L402 {macaroon}:{preimage}") retry_req.add_header("x-agent-id", "autonomous-lp-bot")

with urllib.request.urlopen(retry_req) as final_resp: return json.loads(final_resp.read().decode('utf-8')) else: raise

if name == "main": evaluation = query_risk_oracle() print(f"Risk Level : {evaluation['risk_level']}") print(f"R_net : {evaluation['r_net_pct']:+.4f}%") print(f"Breakeven : {evaluation['breakeven_corridor'}, {evaluation'breakeven_corridor'}]")


Developer Integration

  • Integration cookbook & MCP guides: COOKBOOK.md
  • MCP auto-discovery card: https://api.arsenal-quant.com/.well-known/mcp/server-card.json

Why per-call pricing is the intended model

This engine does not prevent losses, and it makes no claim about how much money it saves you. What it does is compute — deterministically, in $\mathcal{O}(1)$ — whether a position sits above or below its breakeven boundary. Each response carries an HMAC tag over the result, which lets the engine detect tampering with its own output; it is a symmetric provenance marker, not a proof a third party can verify independently.

The intended model is per-call pricing, so the cost can be budgeted like any other input. That model is not yet in service: today every call is free.

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