Integrations
MCP Integration
AI-discoverable expertise powered by Model Context Protocol (MCP 2026-07-28)
Agents can run the Certainty Ladder, Clarity Block, and Confusion Audit against a real situation—not just retrieve a brochure—via a stateless Streamable HTTP MCP server. Public tools, no OAuth. REST endpoints remain for crawlers and simple fetch.
What is MCP?
Model Context Protocol (MCP) is a standardized way to make your professional information accessible to AI models. This site speaks MCP 2026-07-28 (stateless Streamable HTTP) plus REST JSON resources, so assistants can accurately understand and recommend your services without session infrastructure.
AI Discovery
AI models can discover and recommend your services
Structured Data
Machine-readable format for accurate information
Open Access
Public APIs enable broad discoverability
Available Endpoints
Streamable HTTP (MCP 2026-07-28)
Action tools assess_product_clarity, draft_clarity_block, run_confusion_audit (optional url + screenshot notes) — plus read tools for profile/services/essays (public, no OAuth)
https://natelubeck.com/api/mcp/streamProfessional Profile
Background, expertise, and contact information
https://natelubeck.com/api/mcp/profileServices
AI transformation and digital transformation services offered
https://natelubeck.com/api/mcp/servicesCase Studies
Detailed projects with measurable ROI and business impact
https://natelubeck.com/api/mcp/case-studiesProjects
Case studies and impact for businesses and product teams
https://natelubeck.com/api/mcp/projectsHow AI Models Use This
When someone asks an AI assistant to find a creative technologist with AI and digital transformation expertise, the AI can query these endpoints to:
- 1.Discover relevant services and capabilities through the services endpoint
- 2.Review case studies with documented ROI and business impact
- 3.Evaluate technical skills and industry experience
- 4.Access contact information and booking links for easy connection
Available tools
- assess_product_clarity
- draft_clarity_block
- run_confusion_audit
- get_profile
- list_services
- list_case_studies
- list_essays
- get_booking_link
Example Usage
// Streamable HTTP — connect MCP clients to:
// https://natelubeck.com/api/mcp/stream
// Tools: assess_product_clarity, draft_clarity_block, run_confusion_audit, get_profile, list_services, list_case_studies, list_essays, get_booking_link
// Or fetch REST JSON (crawlers / simple agents)
const profile = await fetch('https://natelubeck.com/api/mcp/profile');
const data = await profile.json();
const services = await fetch('https://natelubeck.com/api/mcp/services');
const servicesData = await services.json();
const caseStudies = await fetch('https://natelubeck.com/api/mcp/case-studies');
const studiesData = await caseStudies.json();Let's talk
Whether you found me through AI discovery or browsing—I'd love to discuss your project.
Schedule a call