Why it matters
Most AI-visibility fixes are ordinary page changes: a section that answers the buyer’s question directly, a fact brought up to date, a page for a market that had none. The slow part is the handoff. The analysis lives in one tool, the spec in a document, the change in a repository, and the result nowhere.
The Model Context Protocol is an open standard for connecting tools to AI assistants and coding agents. With it, the gap, the spec, the change and the measurement can all happen where the code is, and the agent that writes the fix can also log it.
The workflow
Pick one gap, not ten
Choose the single question where competitors appear and you do not, and the page on your site that should answer it. One change per experiment is the only way to read the result later.
Write the spec before the prompt
Target page, the questions it should answer, the requirements (short answer first, local facts, local currency, the brand facts it may use) and nothing else. This is what the coding agent gets; if the spec is vague, the change will be too.
Take a baseline before you ship
Measure the target questions for about two weeks before the change, and measure a set of control questions you will not touch. Without a control you cannot tell your change from a surface changing its behaviour the same week.
Ship it as a PR a human reviews
Let the agent draft the change in your repository, then review it like any other pull request. Nothing publishes on its own.
Log the ship date and wait out the window
Record the PR or commit URL and the date, then observe for a set window with at least 20 answers per window. Read the outcome as likely positive, negative, neutral or inconclusive, and say “moved” rather than “caused”: a rate can rise for reasons that have nothing to do with you.
Run the loop over MCP with VozRadar
VozRadar’s MCP server exposes the gaps, the spec, the fix prompt and the experiment as tools, so the whole sequence runs inside Claude Code, Codex, Cursor or Claude Desktop.
Run it in VozRadar
MCP server + Experiments
- list_opportunities returns your biggest gaps; get_recommendation returns the spec: target page, questions, requirements and approved facts
- render_fix produces a Claude Code, Codex or Cursor prompt, or a checklist, written for your own repository
- log_experiment records the PR or commit URL; get_experiment returns status, lift, confidence and verdict
- Experiments default to a 14-day baseline, an observation window after shipping, control prompts and at least 20 answers per window; verdicts are likely positive (at least +10 points with at least 85% probability), negative, neutral or inconclusive
In the app: Sign in with /mcp in Claude Code (OAuth), or create an API key for CI under Settings → API keys & MCP. The same data is on the project’s Fixes and Experiments pages. Available to early customers; during prelaunch the server is reachable from approved networks only.
claude mcp add --transport http vozradar https://app.vozradar.com/mcp
# then /mcp in Claude Code to sign in
list_opportunities # the biggest gaps
get_recommendation # target page, questions, requirements, approved facts
render_fix artifact_type: "CLAUDE_CODE_PROMPT" # a prompt for the coding agent, for your repo
# ship the change as a PR; a human reviews it
log_experiment # the PR or commit URL; marking it shipped needs confirm_cost: true
get_experiment # status, lift, confidence, verdictEverything here is free except two actions: the content brief, and marking an experiment shipped, which starts measurement runs. Both need confirm_cost: true. Baselines are started in the app, not over MCP.
See how AI sees your brand.
Start with one market. Expand when you're ready.