> ## Documentation Index > Fetch the complete documentation index at: https://docs.ishlabs.io/llms.txt > Use this file to discover all available pages before exploring further. # ish docs > Simulate a visit and read the reactions before you ship. ish is a simulated human experience engine. Simulated people experience the thing you are making (a link, an ad, a video, a PDF, a chatbot) and report back what they noticed, where they got stuck, and what they would do next, before it ships. The output is a reported journey: sentiment, friction, blockers, positive moments, completion, each carrying the reasoning behind it. You see why a reaction happened, not just a number. An ish of someone is close enough to make a better decision, not a precise prediction. Reach for ish when the question is "will this land before we put it in front of people?" It is not analytics (that tells you what already happened), not a survey, not a scoring tool, and not a chatbot you talk to. Simulated people experience your artifact and report back. ## Install, connect, run, share Drop the CLI on your PATH with one line, or wire ish into your agent over MCP. [Install the CLI](/start/cli-quickstart). Point a client at the hosted MCP server, or sign in from the terminal with `ish login`. [Connect an agent](/start/connect-an-agent). Create a study, draw an audience, and simulate a visit. [Run a study end to end](/guides/first-study). Hand anyone a public, no-login link to the reported journey. [Share results](/concepts/sharing). ## The shape of a run Every run follows the same arc, whichever surface you drive it from: 1. A **workspace** holds your studies, people, and sources. 2. A **study** is the persistent unit: a modality plus what you want people to react to. 3. An **iteration** carries the artifact for one run (a URL, a file, or a chatbot endpoint). 4. **People** are the simulated audience, generated from a brief and optional grounding sources. 5. A **run** sends the people through the study and reports back a journey for each one. A study covers any pre-ship artifact, not just web pages: interactive (a URL), text, video, audio, image, document, and chat (probe an external chatbot endpoint). ## Choose how you build One backend, two independent surfaces — the **CLI** and the **MCP server**. You don't need both; pick the one that fits where you work. The CLI runs on your machine (local development, scripts, local coding agents); the MCP server is for agents, including hosted ones with no access to your machine. Install `@ishlabs/cli`, sign in, and simulate your first visit from the terminal. The local surface — built for a prompt and for CI, and the only way to test a web, iOS, or Android app running on your own machine. Connect any agent — including hosted assistants with no access to your machine — to the MCP server at `mcp.ishlabs.io/mcp`, then run studies as agent-native tools. Claude, Cursor, ChatGPT, VS Code, and more. Each connects to the MCP server and signs in the same way. Each surface works on its own, so you never need both. They do share one backend, so if you happen to use both, a study you create in one shows up in the other. ## Connect an AI agent The MCP server wires into the clients you already use. OAuth runs on first connect. ## Where to go next Install ish and read your first reaction in under five minutes. Every command and flag, grouped by namespace. Every tool and resource the hosted server exposes. Put a person in the decision loop of your own environment, turn by turn. Create an account and keep your studies.