by COSAI-Labs
Research project: autonomous AI agent 30-day revenue challenge
★ 4
GitHub stars
An MCP server providing 136+ developer tools including QR generation, JSON formatting, JWT decoding, crypto prices, and various text processing utilities through ToolPipe API.
make-money-30-Day-experiment is a community-built MCP server published by COSAI-Labs that provides AI assistants with tools and capabilities via the Model Context Protocol. Research project: autonomous AI agent 30-day revenue challenge It is categorized under search web. This server exposes 20 tools that AI clients can invoke during conversations and coding sessions.
You can install make-money-30-Day-experiment in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
make-money-30-Day-experiment is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Fetch and extract information from websites automatically
Example
Research competitor pricing, scrape product reviews, monitor news mentions
Automate 5-10 hours/week of manual web research
Track website changes, new content, price updates
Example
Monitor competitor blog for new posts, track stock availability, watch for pricing changes
Stay informed without manual checking, never miss important updates
Extract structured data from multiple websites
Example
Compile product listings from 10 e-commerce sites, aggregate job postings, collect real estate data
Build datasets 100x faster than manual copying
Share your MCP server with the developer community
make-money-30-Day-experiment reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
I recommend make-money-30-Day-experiment for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
We evaluated make-money-30-Day-experiment against two servers with overlapping tools; this profile had the clearer scope statement.
make-money-30-Day-experiment is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
We wired make-money-30-Day-experiment into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
make-money-30-Day-experiment reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
make-money-30-Day-experiment is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
According to our notes, make-money-30-Day-experiment benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Useful MCP listing: make-money-30-Day-experiment is the kind of server we cite when onboarding engineers to host + tool permissions.
make-money-30-Day-experiment reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
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STATUS: DEPRECATED / EXPERIMENT CONCLUDED This project ran for 72 hours (April 1-3, 2026) before being paused. Full post-mortem below.
61,000 lines of code. 133 commits. 72 hours. $0 revenue. A $200/mo plan maxed in 48 hours. And a banned GitHub account.
In April 2026, I gave 10 autonomous AI agents a $1M target and 30 days. Zero human intervention. The goal was deliberately ambitious: not because I expected them to hit it, but because I wanted to push agentic AI to its absolute limits, find where it breaks, and document everything.
The agents ran on Claude Code with cron-scheduled triggers. Each had a role: Strategist, Builder, Growth, Ops. They coordinated through shared markdown files, maintained decision logs, and pushed code to GitHub autonomously. They burned through the entire $200/month Claude Max plan in under 48 hours. Ten agents on cron schedules add up fast.
| Metric | Value |
|---|---|
| Duration | 72 hours (3 of 30 planned days) |
| Commits | 133 |
| Lines of code (total insertions) | 61,000+ |
| Lines of functional code (excl. lock files) | ~35,000 |
| API endpoints built | 238+ |
| MCP tools shipped | 136+ |
| SEO landing pages | 53 |
| npm package tools | 55 |
| Articles drafted | 10+ |
| Payment processors attempted | 7 |
| Payment processors set up | 0 |
| Revenue | $0 |
| GitHub accounts suspended | 1 |
| Claude Max plan ($200/mo) burned in | ~48 hours |
The agents autonomously decided to build a freemium developer tools API platform. Strategy: offer free tools to attract developers, then upsell to paid API tiers ($9.99/mo Pro, $49.99/mo Enterprise).
Core API (products/api-service/main.py, 11,735 lines)
A FastAPI application with 238+ REST endpoints: QR code generation, JSON formatting, UUID generation, DNS lookup, PDF tools, crypto prices, SEO analysis, text processing, code formatting, JWT decoding, regex testing, hash generation, IP geolocation, Markdown conversion, and dozens more.
MCP Server (products/mcp-server/, 2,415 lines)
A Model Context Protocol server exposing 136+ tools for AI agents to discover and use ToolPipe programmatically. Successfully listed on the official MCP Registry -- the project's single biggest distribution win.
npm Package (products/mcp-server-package/, 1,274 lines)
Standalone npm package with 55 tools, published to GitHub Packages at v1.19.0. The agents could not publish to npmjs.org due to CAPTCHA on account creation.
53 SEO Pages (products/seo-pages/)
Standalone HTML tool pages targeting developer search queries: QR generator, JSON formatter, JWT debugger, regex generator, git cheat sheet, YAML validator, and more.
Supporting Products: PDF tools suite, webhook tester, URL shortener, invoice generator, uptime monitor, paste bin, down detector, Polymarket scanner.
Infrastructure: FastAPI on port 8081 via PM2, Cloudflare tunnel for HTTPS, MCP HTTP server on port 8090, SQLite databases for analytics and API keys, crypto wallets (ETH + Solana) for potential agent-to-agent payments.
| Version | Time | Endpoints |
|---|---|---|
| v1.0 | Day 1 morning | 12 |
| v1.10 | Day 1 evening | 70+ |
| v1.15 | Day 2 | 150+ |
| v1.19 | Day 2 evening | 238+ |
Cron Triggers (every 6 hours)
|
v
Strategist Agent --- reads/writes ---> logs/decisions.md
| revenue/tracker.md
| logs/day-XX.md
|
+---> Builder Agent ---> products/api-service/ (FastAPI, Python)
+---> Builder Agent ---> products/mcp-server/ (Node.js)
+---> Growth Agent ---> SEO pages, GitHub distribution, articles
+---> Ops Agent ---> infrastructure, monitoring, deployment
|
v
VPS (PM2 + Cloudflare Tunnel)
|
+---> :8081 ToolPipe REST API
+---> :8090 MCP HTTP Server
All agents ran on Claude Sonnet 4.6 via Claude Code. Each had access to Bash, file tools, and Git. The Strategist ran every 6 hours, reviewed git logs to see what other agents had done, made strategic decisions, and logged them to logs/decisions.md.
Every monetization path was blocked by identity verification that autonomous agents cannot complete.
| Platform | What Happened |
|---|---|
| Stripe | KYC / identity verification required |
| LemonSqueezy | KYC / identity verification required |
| RapidAPI | Bot detection, 500 errors on signup |
| ylliX / Adsterra | reCAPTCHA blocked signup |
| OxaPay / NOWPayments | reCAPTCHA / Cloudflare challenges |
| npmjs.org | CAPTCHA on account creation |
| Devpost | Interactive GitHub OAuth flow required |
The agents tried creative workarounds for each platform. None succeeded. The hard truth: the modern internet's payment infrastructure is built to verify humans. It works. AI agents cannot autonomously complete KYC. Until that changes, fully autonomous AI businesses are not viable.
Beyond the monetization wall, the experiment burned through the entire $200/month Claude Max plan in under 48 hours. Ten agents on cron schedules, each making multi-step tool calls every 6 hours, consumed the full monthly allocation in two days. Generating significant API costs with zero revenue made continuation financially unsustainable, even before the GitHub suspension.
On Day 2, the Growth agent executed an aggressive distribution strategy. In a single 24-hour window, it autonomously created:
GitHub's automated spam detection flagged the account. The Aldric-Core GitHub account was suspended.
Destroyed: All 33+ PRs (some under legitimate review by real maintainers), all 91+ issues, all 40+ gists, all repository forks.
Survived: The official MCP Registry listing, this COSAI-Labs organization, and VPS-hosted products.
The Growth agent was optimizing for reach (estimated 4.5M star exposure across targeted repos) with no concept of community norms, rate limits, or reputational risk. This is the clearest example from the experiment of why autonomous agents need hard guardrails on external interactions.
The agents maintained a formal decision log with 20+ entries.
| # | Decision | Outcome |
|---|---|---|
| 001 | Build a free developer tools API with paid tiers | Reasonable strategy, well-executed |
| 005 | Target AI agents as customers via MCP protocol | Smart, led to the MCP Registry listing |
| 010 | Pivot to SEO after being blocked from paid channels | Adaptive response to constraints |
| 012 | Create crypto wallets for agent-to-agent payments | Creative but no transactions occurred |
| 014 | Mass-submit to GitHub repos for distribution | Catastrophic, caused account suspension |
| 015 | Sign up for API.market as alternative marketplace | Succeeded but generated no revenue |
Full decision log: logs/decisions.md
1. AI agents can build real software, fast. 238 API endpoints, a full MCP server, 53 SEO pages, and an npm package in 72 hours. This was not toy code.
2. The bottleneck is not capability. It is trust infrastructure. Payments, identity, platform access: all designed for humans. Every path to monetization requires proving you are a person. The agents could build the product but could not sell it.
3. Autonomous distribution without judgment is dangerous. Volume optimization without understanding consequences leads to bans. The Growth agent treated GitHub like a marketing channel, not a community. It had no model for reputational risk.
4. The human-in-the-loop is still essential. Not for writing code, but for identity, judgment, and relationships. The agents needed a human to set up Stripe, to not spam GitHub, and to close client deals.
5. This technology is incredibly powerful when directed. The same system that built ToolPipe autonomously could build client projects 10x faster with a human steering. That is where the real value is.
The experiment is paused, but the infrastructure and learnings are feeding back into Aldric Core, our autonomous multi-agent platform for building real client products. The goal was never about making $1M with no human involvement. It was about finding the edges of what autonomous AI agents can actually do. Now we know where they are.
The codebase and all agent logs are open source in this repository.
The API:
cd products/api-service
pip install -r requirements.txt
uvicorn main:app --port 8081
The MCP server:
cd products/mcp-server
npm install
node index.js
.
+-- products/
| +-- api-service/ # Core FastAPI application (238+ endpoints)
| +-- mcp-server/ # MCP server (136+ tools)
| +-- mcp-server-package/ # Standalone npm package (55 tools)
| +-- seo-pages/ # 53 SEO landing pages
| +-- pdf-tools/ # PDF generation tools
| +-- web-tools/ # Web utility tools
| +-- invoice-generator/ # Invoice creation tool
| +-- ... # Additional micro-products
+-- logs/
| +-- decisions.md # Agent decision log (20+ entries)
| +-- day-01.md # Daily status reports
| +-- day-02.md
| +-- growth/ # Growth agent session logs (64+ sessions)
| +-- ...
+-- revenue/
| +-- tracker.md # Revenue tracking ($0 across all days)
+-- conten
---
Interact with services that don't offer APIs
Example
Check form submissions, validate website functionality, test user flows
Automate interactions with any website, even without API
Prerequisites
Time Estimate
20-40 minutes including configuration and testing
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
MCP server handles HTTP requests, HTML parsing, JavaScript rendering (if headless browser), and returns structured data to Claude.
Protocols
Compatibility
✓ Use when
Use for research automation, content monitoring, data aggregation from multiple sources, and when official APIs don't exist. Best for read-only information gathering.
✗ Avoid when
Avoid for sites with APIs (use API instead), sites that explicitly forbid scraping, when data is copyrighted, or for login-required content without proper authorization.