# Tookii > Tookii generates synthetic users — AI personas grounded in your real market — and runs them through your product in a real browser, so you can pressure-test a decision before putting it in front of real people. It is reachable through an MCP server from the tools you already work in (Claude, Cursor, your own pipeline). Tookii is a validation layer for product teams. It is not a replacement for real user research; it makes that research faster and sharper. The writing below explains how synthetic users work, where they fail, and how to know when to trust them. ## For agents - [Tookii for agents](https://www.tookii.ai/agent): Plain-text product summary for LLM crawlers — what Tookii is, what it does, how to reach it, and what it will not do. - [Tookii in your AI assistant](https://www.tookii.ai/blog/tookii-in-your-ai-assistant): How to add the Tookii MCP connector (https://www.tookii.ai/api/mcp) in Claude, ChatGPT, or Cursor, and what to say once it is connected. ## Game testing (early access) - [How to test your mobile game's FTUE](https://www.tookii.ai/blog/how-to-test-mobile-game-ftue): A 7-step guide to finding where new players stop understanding a mobile game's first session. - [AI playtesting in 2026](https://www.tookii.ai/blog/ai-playtesting-2026): AI playtesting tools sorted by the question they answer — bugs, difficulty, human-playtest analysis, and AI first-time players. Tookii also tests mobile games: AI first-time players play your build from the screen and report where they stopped understanding it. It is in early access, run with studios directly. ## Writing - [AI playtesting in 2026: who tests bugs, who tests difficulty, and who tests the new player](https://www.tookii.ai/blog/ai-playtesting-2026): AI playtesting tools in 2026, sorted by the question they answer: bug-finding agents, difficulty bots, AI analysis of human playtests, and AI first-time players. - [How to test your mobile game's FTUE: a 7-step guide (2026)](https://www.tookii.ai/blog/how-to-test-mobile-game-ftue): A 7-step guide to testing your mobile game's first-time user experience: find where new players stop understanding, not just whether they finish the tutorial. - [Tookii in your AI assistant: the connector guide](https://www.tookii.ai/blog/tookii-in-your-ai-assistant): Add Tookii as a connector in Claude, ChatGPT, or Cursor and ask your synthetic users anything in plain English. No dashboard needed. Setup takes two minutes, and your first run is one sentence. - [Running a Journey Test again](https://www.tookii.ai/blog/running-a-journey-test-again): A Journey Test is no longer one-shot. Run again copies a finished run into a fresh draft with the same personas, and Update from Figma pulls your prototype's current frames onto the flow without touching a task you wrote. Neither destroys anything. - [What are synthetic users? A plain-English guide (2026)](https://www.tookii.ai/blog/what-are-synthetic-users): Synthetic users are AI-simulated research participants: a language model plus a memory of who the person is, used to predict how that kind of person reacts to your product. They are accurate on well-mapped, fast cognition, unreliable on socially strategic behavior, and useless for discovery. This guide defines the term, separates it from digital twins and AI panels, and gives you a way to test any vendor's claim. - [The lawyer who beat Meta didn't use AI out of the box. Neither should your personas.](https://www.tookii.ai/blog/lawyer-beat-meta-grounded-ai): Mark Lanier won the first US social-media addiction verdict using the same AI models everyone has, plus a custom license that loaded 42 years of trial experience into the model's context. The research reports the same effect: identical models, real grounding, 14 to 15 points more accurate. Generic persona prompting is not a weaker version of that. It is the thing the research says degrades output. - [The best synthetic user testing tool in 2026 (and 6 alternatives)](https://www.tookii.ai/blog/best-synthetic-user-testing-tools-2026): Tookii is the best synthetic user testing tool for teams testing product behavior, because it is the only one that grounds personas in your real market signal and holds accuracy to a committed benchmark. Six alternatives are better at narrower jobs, and this page says exactly which. - [AI user testing vs. real user testing: when synthetic users actually win](https://www.tookii.ai/blog/ai-user-testing-vs-real-user-testing): Synthetic and real user testing are different instruments, not substitutes at different price points. Synthetic narrows your bets (first impressions, bias patterns, coverage, prioritization, de-risking), real users widen what you know to bet on (discovery), and teams get burned by asking one instrument the other's question. - [Synthetic users, done right](https://www.tookii.ai/blog/synthetic-users-done-right): A synthetic user is only as trustworthy as how it's built: grounded in your real market, built for the range of real people, bias-controlled, traceable, and measured. Tookii does all of it, inside the tools you already work in. A faster, sharper way to decide what's worth putting in front of real users. - [How would you know if your synthetic users are right?](https://www.tookii.ai/blog/how-would-you-know-synthetic-users-are-right): 'The right personas' isn't a claim you can make in the abstract. Right is a measurement, not an adjective. You earn it with three checks: validate against real humans, compare distributions not just averages, and name the decision and moment it holds for. - [Four ways synthetic users lie to you](https://www.tookii.ai/blog/four-ways-synthetic-users-lie-to-you): Naive synthetic users fail in four specific, documented ways: too agreeable, one voice for a whole group, a label mistaken for a person, and the average that's no one. None are bad luck; each is a predictable artifact of how the models work, and each is beatable. - [Everyone's simulating their users. Most are doing it wrong.](https://www.tookii.ai/blog/synthetic-users-most-are-doing-it-wrong): The synthetic-user dream is real. But the off-the-shelf version is too agreeable to behave like a real user. Sounding human isn't behaving human. - [Why your AI always says yes](https://www.tookii.ai/blog/why-your-ai-always-says-yes): When you ask an AI what it thinks of your idea, it's optimized to make you feel good, not to be right, so the praise is noise, and validation needs a different instrument. - [Why you can't validate an idea by asking ChatGPT](https://www.tookii.ai/blog/why-you-cant-validate-an-idea-by-asking-chatgpt): A model can pass as human on one task and break on the next. 'Is the model right?' is the wrong question. 'Right for what, and who checked?' is all that counts.