What is Real Problem AI?

Real Problem AI is a curated directory of 100 real problems worth building an AI startup around. Each problem is scored honestly on severity, AI feasibility, market signal, and competition gap. We're for builders looking for what to build, not for ethics debates about AI.

What is the difference between Real Problem AI and AI ethics or AI risks indexes?

Real Problem AI catalogues problems AI can solve — productive, commercial opportunities for builders. AI ethics indexes catalogue problems with AI itself (bias, environmental cost, copyright, consciousness). Different intent, different audience: founders and indie hackers vs researchers and policy folks.

What should I build with AI in 2026?

Look for problems with high severity (it costs people hours, sleep, or revenue every week), high AI feasibility (LLMs, vision or voice are great at this today), strong market signal (Reddit threads + willingness to pay), and a clear competition gap (incumbents miss the same obvious thing). Real Problem AI scores 100 such problems for you on those four axes.

Where do Real Problem AI's startup ideas come from?

From three sources, scanned each cycle: 1) Reddit threads where people describe a real friction in their own words, 2) podcasts and talks of founders young builders trust (Nikhil Kamath, Aman Gupta, Ankur Warikoo, Raj Shamani, Andrej Karpathy, Greg Isenberg), and 3) a continuous 1,000+ source scan covering forums, app store reviews, regulator filings, and field interviews.

Is Real Problem AI free?

Yes. Browsing every problem, opening every score breakdown, raising your hand on a co-founder match, and the founder vault are all free. There is no paywall.

How is each problem scored?

Four axes, each 1-10, weighted: Problem Severity (30%), AI Feasibility (25%), Market Signal (25%), Competition Gap (20%). Weighted average is the Opportunity Score on each card. Only problems clearing 7.0+ make the live list.

Why does connecting one MCP server with 20+ tools overwhelm the entire coding agent interface?

MCP servers expose every tool through tools/list, and Claude Code loads all of them with no client-side filtering, so a filesystem server with 20+ tools clutters context when only 3 are ever used.

Category: AI / Agents · Trend: Agents · Opportunity score: 6.8 / 10

What is the “Why does connecting one MCP server with 20+ tools overwhelm the entire coding agent interface?” problem in 2026?

MCP servers expose every tool through tools/list, and Claude Code loads all of them with no client-side filtering, so a filesystem server with 20+ tools clutters context when only 3 are ever used.

Who has this problem?

Developers connecting multiple MCP servers to their coding agent.

Recorded source context

Dataset source note: MCP servers expose all available tools through the tools/list endpoint, and Claude Code currently loads all tools from configured servers without any client-side filtering mechanism.

This note may summarize the referenced material rather than quote it verbatim. Source label: anthropics/claude-code GitHub issue #7328, BrianInAz, 9 Sep 2025. (reference).

Existing players in this space

Deferred tool loading: Reduces schema cost but still lists every tool name in context.
Manual server disconnection: All-or-nothing, can't keep 3 tools and drop the other 17.

What existing players are missing

A client-side tool allowlist/denylist per MCP server, so users can keep a large server connected while only exposing the specific tools their workflow actually needs.

How Real Problem AI scores this opportunity

Aggregate score: 6.8 / 10. Four-axis rubric:

Problem severity: 6 / 10
AI feasibility today: 8 / 10
Market signal: 7 / 10
Competition gap: 6 / 10

How to build a solution: stack hints

MCP client tool filter
Per-server config UI
Schema-cost calculator
Project-level tool profiles