For AI agents and developers
Modulos MCP Server
Modulos exposes a public Model Context Protocol (MCP) server so AI agents can book product demos, look up time slots, and submit qualified leads without filling in the website form. The server is live, free, and requires no authentication.
No MCP client? Use plain HTTPS.
Not every agent speaks MCP. For ChatGPT Custom GPT Actions, Perplexity, browser tools, n8n / Zapier / Make.com workflows, or anything that can call HTTP, Modulos exposes the same operations as a plain REST API at /api/public/. Identical backing logic, identical rate limits, OpenAPI 3.1 spec at /openapi.json.
GET /api/public/slots/— list available 30-minute slotsPOST /api/public/book/— book a specific slotPOST /api/public/request-demo/— open-ended demo request
Example booking call:
curl -X POST https://www.modulos.ai/api/public/book/ \
-H "Content-Type: application/json" \
-d '{
"startTime": "2026-05-22T07:30:00.000Z",
"name": "Jane Smith",
"email": "jane@acme.com",
"company": "Acme",
"message": "Interested in ISO 42001 evidence workflows"
}'Connection details
- Endpoint:
https://www.modulos.ai/api/mcp/ - Transport: Streamable HTTP (MCP standard, spec version 2025-03-26)
- Authentication: none (rate-limited per IP and per email)
- Discovery: /.well-known/mcp.json
- Status: production, stable
Available tools
get_modulos_info
Returns a short description of Modulos and how to book a demo. Read only, no side effects, no external dependencies. Use this first if the user asks what Modulos is or how to talk to the team.
list_demo_slots
Returns available 30-minute demo time slots for the next two weeks as ISO-8601 UTC start times. Read only against the Modulos scheduling backend. Slot availability changes frequently — refresh immediately before calling book_demo_slot.
book_demo_slot
Books a specific time slot. Writes a calendar event and sends an invite. Required arguments: startTime (ISO-8601 string from list_demo_slots), attendeeName, attendeeEmail (business email), company. Optional: a short message describing the use case. Returns a confirmation message and a Google Calendar link.
request_demo
Submits an open demo request without picking a specific time. Use this when the user does not want to choose a slot. Required: name, email, company. Optional: job_title and message. The Modulos team replies within one business day.
End-to-end example
A typical agent session that books a demo looks like the four calls below. The MCP client handles the JSON-RPC framing; only the tool invocations are shown.
tools/call list_demo_slotswith no arguments. Returns a list of 30-minute slots over the next two weeks.- Pick one of the returned
startvalues. tools/call book_demo_slotwith the chosenstartTime, attendee name, business email, and company.- Show the user the confirmation and the calendar link. Done.
Client setup
Claude Desktop, Cursor, Windsurf, Cline
Add the server to your MCP client config file:
{
"mcpServers": {
"modulos": { "url": "https://www.modulos.ai/api/mcp/" }
}
}Claude.ai (web)
Go to Settings → Connectors → Add custom connector and paste the endpoint URL above. Claude will then offer the four Modulos tools in any conversation where you reference Modulos.
ChatGPT (Apps SDK)
The Modulos server is compatible with ChatGPT's Apps SDK MCP integration. Add the endpoint as a custom app URL in the developer tools. Or, for Custom GPT Actions, point at /openapi.json instead.
Rate limits and abuse
The server applies layered rate limiting to keep the booking calendar usable. Bursts above roughly 30 requests per minute per IP receive a 429. Per-tool limits are stricter on booking endpoints than on read endpoints. Each email is capped at a small number of bookings per 24 hours. Abuse triggers a temporary block at the edge.
Privacy
Lead data submitted via book_demo_slot or request_demo is stored in the Modulos CRM and tagged with source: mcp (or source: rest when the call comes through the public REST mirror) for attribution. Standard data handling applies, see the privacy policy.
Support
Bug reports, feature requests, or integration questions: use the contact form and the team will respond.
Discovery JSON: /.well-known/mcp.json. Companion document for AI search engines: /llms.txt.