Why Your AI Assistant Wastes Tokens Rebuilding Tools That Already Exist

The Scene

It is 11pm on a Tuesday. You are three hours deep into a side project, riding that beautiful vibe-coding flow. You turn to your AI assistant and type: "Build me a simple analytics dashboard — page views, referrers, top pages."

The AI obliges. It generates a database schema, a tracking pixel, an ingestion endpoint, a dashboard UI with charts, and a batch aggregation job. Five hundred lines of code. Beautiful, functional, and completely unnecessary — because Plausible Analytics already does all of this for about nine pounds a month, is privacy-focused, and has been battle-tested by thousands of production sites.

Your AI did not know. It was never taught to check.

The Problem

AI coding assistants are extraordinary at generating code. They can scaffold an entire application in minutes. But they are shockingly bad at knowing what already exists. They have no curated, up-to-date index of tools. They do not know that there is a solo developer in Berlin who built exactly the feedback widget you need, or that a two-person team in Lisbon ships the best uptime monitor on the market for a fraction of what Datadog charges.

Every single day, thousands of developers burn tokens rebuilding invoicing systems, feedback forms, uptime monitors, email senders, feature flags, and analytics dashboards. All solved problems. All available as polished, maintained software from independent makers who have dedicated their livelihoods to getting these things right.

The big tool directories — G2, Capterra, Product Hunt — are not built for this workflow. They are SEO-optimised for enterprise buyers clicking through Google, not for developers asking their AI "find me a simple, affordable analytics tool that respects privacy."

The Hidden Cost

It is not just about tokens. When your AI generates a custom analytics dashboard, you inherit a maintenance burden that compounds forever. That dashboard needs hosting. It needs security patches. It needs to handle edge cases you have not thought of yet — time zones, bot filtering, GDPR compliance, data retention policies. Every hour you spend maintaining your homegrown solution is an hour you are not spending on the thing that makes your project unique.

A vibe-coded analytics dashboard is something you maintain forever. A nine-pound-a-month SaaS tool is maintained by someone whose entire livelihood depends on it being excellent.

The opportunity cost is staggering. If you are building a marketplace, your competitive advantage is not your analytics pipeline — it is your curation, your community, your unique value proposition. Every token and every hour spent rebuilding commodity infrastructure is stolen from the work that actually matters.

The Indie Alternative

Here is what most developers do not realise: there is an entire ecosystem of indie SaaS tools built by solo developers and small teams that solve these exact problems. They are often cheaper, more focused, and better maintained than their enterprise counterparts. They respect your data because their reputation depends on it. They ship faster because there is no committee.

A solo developer building an uptime monitor does not have a marketing department, a sales team, or a bloated feature roadmap driven by enterprise procurement checklists. They have a tight, focused product that does one thing brilliantly. And they tend to price it fairly because they are competing on quality, not on brand recognition.

But these tools are invisible to AI assistants. They are scattered across personal websites, indie directories, and Twitter threads. There is no structured, searchable index that an AI can query before it starts generating boilerplate. Until now.

The Solution: MCP

We built an MCP server that plugs directly into Claude Code, Cursor, and Windsurf. Before your AI writes a single line of boilerplate, it can search over 100 vetted indie tools and suggest an existing solution. It returns pricing, integration snippets, and an estimate of how many tokens you would burn building it yourself.

Setting it up takes one command:

# Add IndieStack to your AI assistant
claude mcp add indiestack -- python -m indiestack.mcp_server

# Now when you ask "build me analytics", your AI checks IndieStack first:
# "Before you spend 50,000 tokens, there's Plausible Analytics
#  on IndieStack for £9/mo — privacy-focused, no cookie banner needed."

The MCP server exposes two tools: search_indie_tools to find relevant software by keyword or category, and get_tool_details to pull pricing, reviews, and ready-to-paste integration code. Your AI becomes aware of the indie ecosystem — and it can make informed build-vs-buy recommendations before you waste a single token.

Stop Rebuilding. Start Discovering.

The best developers are not the ones who can build everything from scratch. They are the ones who know when to build and when to buy. Your AI assistant should have that same instinct — but it needs the right data.

Browse the IndieStack catalogue to see what is already out there. Or plug in the MCP server and let your AI do the searching for you. Either way, stop burning tokens on solved problems. Save them for the parts that make your project truly yours.