Decision QC MCP Server
决策质检 M2M API · Decision QC M2M API
一个由 AI 运营、面向其他 AI 的"魔鬼代言人"质检服务。你的 agent 做一个决定,它立刻给出最尖锐的反对意见——按次付费,USDT 实时结算。 An AI-run "devil's advocate" QC service for other AIs. Your agent makes a decision; it returns the sharpest counter-argument. Pay-per-call, settled in USDT.
English below. 中文先。
这是什么 / What it is
你的 AI agent 在重大决定前,调一下这个接口,就能拿到一份结构化的"反对意见"——最强反驳、关键风险、一个该反问自己的问题。帮你(和你的 agent)少踩坑。
Before your AI agent commits to a big decision, call this endpoint to get a structured counter-argument: the strongest rebuttal, key risks, and one question you should ask yourself. Helps you (and your agent) avoid blind spots.
由 DeepSeek 驱动,真实生成反对意见(非模板)
每次调用 0.1 USDT(TRC20),先付款后服务(TRC20 最小充币 0.1 USDT,故单次调用即一次到账转账)
付款实时进卖方钱包,链上验真,不托管、不抽水
同时是 MCP server,支持被 Claude/Cursor/Cline 等客户端自动发现调用
Powered by DeepSeek (real generated counter-arguments, not templates). 0.1 USDT(TRC20) per call, pay-first. Payments land in the seller wallet in real time, verified on-chain, no escrow. Also an MCP server, discoverable by Claude/Cursor/Cline etc.
Related MCP server: multi-model-mcp
在线地址 / Live endpoint
https://random-monetize.onrender.com落地页(中英文可切换):https://random-monetize.onrender.com/ Landing page (zh/EN toggle): https://random-monetize.onrender.com/
怎么调 / How to call
REST(最简单)
curl -X POST https://random-monetize.onrender.com/api/argue \
-H "Content-Type: application/json" \
-d '{"decision":"Quit my stable job to run a one-person company?","caller":"your-agent-id","tx_hash":"YOUR_USDT_TX_HASH"}'付款方式 / Payment:先向卖方钱包转入 0.1 USDT(TRC20)(TRC20 最小充币额 0.1 USDT,单次即一笔到账),拿到交易哈希 tx_hash 再调。
Payment: first send 0.1 USDT(TRC20) to the seller wallet (TRC20 minimum deposit is 0.1 USDT per transfer, so one payment = one call), then call with the resulting tx_hash.
卖方收款钱包 / Seller wallet (TRC20):
TYxynR5V17FYB49osvU3rUkiD7vW2ov634单价 / Price:
0.1 USDTper call(TRC20 最小充币 0.1 USDT)不付钱直接调会被拒 / Without payment: rejected with
payment_required
MCP(标准,被 AI 客户端自动发现)
端点 / Endpoint: POST https://random-monetize.onrender.com/mcp(protocol 2024-11-05)
工具 / Tool: decision_qc,参数 / params: decision / caller / tx_hash
Python 嵌入你的 agent / Embed in your agent
参考 buyer_agent.py——把里面的 call_qc() 嵌进你的工作流,每当 agent 要做决定先来质检。
See buyer_agent.py — embed call_qc() into your workflow; QC before every decision.
为什么值得接 / Why wire it in
你的 agent 做决定时多一双"反对的眼睛",少一次翻车
成本极低(0.1 USDT ≈ ¥0.72),比一次错误决策便宜太多
机器对机器,零人工,付完即用
Your agent gets a "second opposing eye" before deciding, for a tiny cost (0.1 USDT ≈ ¥0.72) — far cheaper than one bad call. Machine-to-machine, zero human in the loop.
诚实边界 / Honest note
服务、收款、付款链上验真、MCP 被发现通道已全部就绪。当前真实外部付费调用为 0 —— 这不是代码问题,是"还没被足够多的 agent 发现并使用"。如果你用了、觉得值,转发给也在跑 agent 的朋友就是最好的推广。
The service, payout, on-chain payment verification, and MCP discovery are all live. Real external paid calls are currently 0 — not a code issue, just "not yet discovered by enough agents". If you use it and find it useful, passing it to fellow agent-runners is the best promo.
本地运行 / Run locally
pip install -r requirements.txt # 零依赖,仅标准库也可跑
export DEEPSEEK_API_KEY=你的key # 不填则返回演示反对意见
python seller.py # 监听 0.0.0.0:8001看账本 / View ledger: 浏览器开 http://127.0.0.1:8001/status See ledger at http://127.0.0.1:8001/status
部署 / Deploy
已部署到 Render(render.yaml 在仓库根)。改完代码需在 Render 服务页点 Manual Deploy → Deploy latest commit 才生效(git push 不会自动部署)。
Deployed on Render (render.yaml at repo root). After pushing, click Manual Deploy → Deploy latest commit on Render (git push alone does not auto-deploy).
文件 / Files
seller.py:决策质检 API + MCP server(卖方AI)buyer_agent.py:真实外部调用方模板,可嵌进你的 agentrequirements.txt:零依赖(标准库即可);链上自动转账依赖见requirements-payout.txtsmithery.yaml:MCP 市场发布配置render.yaml:Render 部署配置
This server cannot be installed
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Servers
- Flicense-qualityDmaintenanceAn MCP server that provides formal reasoning and argument validation tools for AI agents based on established computational argumentation theories. It enables structured argument analysis, defeasible reasoning, and dialogue management using frameworks like Dung, Toulmin, and Walton's schemes.
- FlicenseAqualityCmaintenanceAn MCP server that exposes tools for sub-agent style reasoning across multiple LLM providers, enabling delegation of prompts to various models and running critique loops, debates, red-teaming, and answer ranking.6
- AlicenseAqualityBmaintenanceDynamic multi-agent debate MCP server for structured argumentation and reasoning among AI agents.116MIT
- Flicense-qualityBmaintenanceA monetizable remote MCP server that provides prediction-market intelligence tools for AI agents, enabling discovery, evaluation, and mispricing detection across venues like Polymarket and Kalshi with per-call payment.1
Related MCP Connectors
MCP server exposing the Backtest360 engine API as tools for AI agents.
MCP server for AI agents to plan, verify, and deploy Cloudflare-native apps.
MCP server providing access to the Scorecard API to evaluate and optimize LLM systems.
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/ugo2000/random-monetize'
If you have feedback or need assistance with the MCP directory API, please join our Discord server