AgentProfiles

AgentProfiles is a prototype for the profile-aware web: trusted services can use the context a person chooses to share with their agent, then carry out a confirmed action through WebMCP. The live demonstration uses decision-ready business profiles to compare location, pricing, availability, and documented specialties before booking.

Business profiles made the web legible to people. AgentProfiles explores what happens when a user-controlled profile makes it legible to agents.

Why WebMCP

WebMCP lets a website provide structured, well-described tools to an AI agent running in the browser. Instead of guessing at UI controls, an agent can use three business-profile tools directly. The agent combines these trusted actions with context the user has already provided and confirms before it books:

Tools are registered by components/webmcp-tools.tsx at component mount, use native navigator.modelContext on supported secure browsers, and dynamically load @mcp-b/global as a fallback. Booking mutations go through the same Next.js API and atomic Supabase RPC as the regular UI. This prototype does not claim access to a private OpenAI profile API; it demonstrates the product experience of user-controlled context plus trusted site tools.

Related MCP server: Cal.com MCP Server

Local setup

1. Create a Supabase project and copy .env.example to .env.local.

2. Add the Project URL, anon key, and service-role key. Never expose the service-role key to the browser.

3. Run supabase db push, then execute supabase/seed.example.sql in the Supabase SQL editor.

4. Install and run:

npm install
npm run dev

This release includes a fully fictional, runnable public seed dataset. It is deliberately different from the live demonstration data and contains no real people or customer records.

Test the agent flow

1. Serve the app over https (or localhost during development).

2. Open it in the ChatGPT Desktop built-in browser.

3. Open the site-tools menu in the browser address bar to inspect the three registered tools.

4. Ask for an appointment in your seeded city and category. The agent can use the returned neighbourhood, pricing guidance, availability, and specialties to explain trade-offs.

5. Search a specialty such as perm to test business-fit reasoning.

6. Confirm a selected slot and verify it disappears from availability.

Chrome testing requires WebMCP enabled through the experimental flag/origin trial. The MCP-B fallback is also available in browsers without native support.

Deploy

Vercel

Import the repository, set the three Supabase variables from .env.example, and deploy. Next.js config needs no extra changes.

Render

Create a Blueprint deployment from render.yaml, add the three Supabase variables, and deploy.

Submission copy

AgentProfiles is a first look at the profile-aware web. It gives AI agents trusted, structured business profiles—not scraped pages—with precise locations, prices, appointment availability, and documented specialties. The agent can weigh proximity, cost, and service fit while the person remains in control: booking only happens after explicit confirmation through a transparent WebMCP action.