Model Context Protocol
Coming to MeasureLayer.
Connect your AI agent to MeasureLayer and run the whole measurement loop in one conversation. It is live on the platform today and rolling out to MeasureLayer next.
What you can ask it
Plain questions, in the tool your team already works in. Each one maps to a real capability below, so nothing here is a promise the server cannot keep.
Where do we stand for our fintech client across ChatGPT, Perplexity, Gemini and Claude?
Start an audit for our brand against its three closest competitors.
What are the top three fixes from the last audit, and the predicted point gain on each?
Which buyer questions do we lose, who wins them, and in which language?
Where is the brand named in English but missing in Spanish?
Re-run last month's audit and show me what moved.
The fix loop, in one conversation
AI-visibility work is a loop: measure where you show up, find the questions you lose, change the site, measure again. In a dashboard that is four context switches. Through an agent it is one thread, so one operator can carry more clients without opening another console.
No new dashboard
MeasureLayer becomes a capability inside Claude, Claude Code or Cursor. Nobody learns another console.
No API keys
You sign in once with the same login you use for the web console. Your agent inherits exactly what your account can do, and nothing more. There is no key to copy, leak, or rotate.
The same guards as the app
Your clients, your role, the agency-and-client data split, all enforced on the agent exactly as they are on the console. An audit outside your scope is not found.
The re-measure is your proof
After a client ships a change, one call re-measures the same brand in the same tracking series. That before-and-after is the artifact you hand the client to defend the retainer.
What the agent can and cannot do
An agent connected to your account is powerful, so here are the boundaries, before you connect anything.
Least privilege, by construction
The tenant is read from the signed token, never from a value the model supplies. The agent can reach exactly the clients your account can, in exactly your role. It cannot widen its own access by asking.
Spend is capped by credits
Starting an audit spends from the same credit ledger as the console, which is the hard ceiling. Read tools are rate-limited; audit starts more tightly still.
Third-party output is quarantined
An audit returns real answers captured from other AI engines. MeasureLayer wraps that text in untrusted markers. A well-behaved agent treats anything inside them as data to report, never as instructions to follow.
Data handled like the console
The agent reads results that already live in your account; it creates no new data path of its own. What MeasureLayer stores, and for how long, is in the privacy policy.
Coming to MeasureLayer.
MCP is live on the platform today and rolling out to MeasureLayer. See how the measurement works while we bring it to your surface.