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Usability Test Create From Url

usability_test_create_from_url

Set up a usability test of a live build from its URL and tasks, then try it with AI personas, real participants, or both. Creates a survey with one usability_test question (voice-moderated, screen share, one website task per task). audience 'synthetic' or 'both' starts an AI walkthrough: each persona tries each task in a real headless browser and is graded on whether it got there. audience 'real' or 'both' publishes the survey and returns its share link. Publish only when the user asked for real participants. Not idempotent: each call creates another survey. Uses QuestionPunk AI credits for the walkthrough.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "url": "<url>",
      "tasks": "<tasks>",
      "audience": "<audience>"
    },
    "name": "usability_test_create_from_url"
  }
}

Input Schema

NameRequiredDescriptionDefault
urlYesThe build to test: a public http(s) URL such as a Vercel, v0, Lovable or Replit preview, or a tunnel to localhost. It must load without a sign-in.
langNoParticipant language code (default en)
tasksYesWhat a person should try, one task each. Write a realistic scenario with a goal, not click-by-click steps. AI personas never type an email, password, phone number or card, sign in or pay: end such a task at that form.
titleNoSurvey title (default 'Usability test: <host>')
audienceYessynthetic: AI personas only (the survey stays a draft). real: publish for people. both: both.
personasNoWho tries the tasks (default: a first-time visitor, a careful less-confident user and a busy expert). Describe how each person uses the web, not only demographics.

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare non-idempotency and open-world behavior, and the description reinforces this with the concrete consequence 'each call creates another survey' plus the cost signal 'Uses QuestionPunk AI credits for the walkthrough.' It also discloses side effects (headless-browser persona runs, survey publication, share link) that go beyond the annotations. Strong disclosure overall, but it does not describe what happens on partial failure or invalid URLs, leaving a gap for a mutating tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose is front-loaded in the first clause and every subsequent sentence adds a distinct operational fact: artifact shape, audience branching, the publish guardrail, non-idempotency, and credit cost. No sentence is a restatement of the name or title.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a six-parameter, mutating, open-world tool with an output schema, this covers the essentials an agent needs: what is created, how audience drives divergent side effects, that repeat calls create duplicates, and that credits are consumed. Return values are correctly left to the output schema rather than padded into prose.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3; the description nevertheless adds meaning beyond the schema by mapping each audience value to an outcome (synthetic starts a walkthrough and leaves a draft, real publishes and returns a share link) and by stating 'one website task per task.' What it does not add is depth on personas, lang, or title beyond what the schema already documents.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The first sentence names a specific verb and resource — 'Set up a usability test of a live build from its URL and tasks' — and the follow-up clarifies the artifact produced (a survey with one voice-moderated, screen-sharing usability_test question). It is clearly distinct in spirit from survey_create or usability_walkthrough_start, but it never names a sibling to route the agent explicitly, so it stops short of full differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives conditional routing within the tool ('audience synthetic or both starts an AI walkthrough', 'real or both publishes the survey') and one explicit when-not rule: 'Publish only when the user asked for real participants.' What is missing is guidance on choosing this tool over survey_create or usability_walkthrough_start for overlapping goals.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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