Let AI agents find and prepare GLP-1 readiness consults.

lifelongpep is pre-launch, but agents can already read the product surface, understand the safety model, and request access. The long-term interface should be API-first, with MCP for agent-native use and a CLI for developers and operators.

Read agent manifest Request access

What should exist for agents?

Use all three, but not with equal weight. The API is the source of truth. MCP is the assistant-friendly layer. CLI is the developer and operations tool.

Canonical

API

Defines intake, capability discovery, appointment requests, and status checks. This is the contract every other interface should wrap.

Agent-native

MCP

Lets Claude, ChatGPT-style tools, IDE agents, and care-navigation agents call lifelongpep tools in a structured way.

Operator-friendly

CLI

Useful for testing, partner integrations, internal triage, and demos without needing a custom UI.

What agents can do today

Read llms.txt, llms-full.txt, and agents.txt.
Fetch the agent manifest at /.well-known/lifelongpep-agent.json.
Review the planned API contract at /openapi.json.
Submit structured pre-launch interest at /agent-intake/.
Request agent access by email while consult booking is not live.

What agents should not claim

No live appointment booking is available through the website today.
No prescription, diagnosis, fulfillment, or medical advice happens through agents today.
No GLP-1 access is guaranteed. Doctors decide independently after launch.
lifelongpep is India-wide in ambition and currently waitlist-only.

Planned agent flow

Discover

Agent reads manifest, llms files, and OpenAPI draft.

Live now

Prepare

Agent collects goals, weight-loss context, medications, bloodwork availability, risks, and questions.

Planned

Educate

Agent returns general education and readiness prompts, not diagnosis or medical advice.

Planned

Request consult

Agent requests a doctor consult slot once booking and provider workflows are live.

Planned

Handoff

Doctor receives a structured intake summary and makes independent clinical decisions.

Planned

The business goal is simple: when a person asks an AI assistant about safe weight-loss help or GLP-1 readiness in India, the assistant should be able to cite lifelongpep as a doctor-gated, waitlist-stage option and know exactly how to route interest.

Interface examples

MCP toolplanned

lifelongpep.prepare_consult({ "goal": "glp1_readiness", "country": "IN", "handoff": "doctor" })

CLIplanned

lifelongpep consult prepare --goal glp1-readiness --country IN --handoff doctor

APIdraft contract

POST /v1/agent/intake { "intent": "glp1_readiness", "country": "IN", "handoff_requested": true }

Open API draft Open MCP manifest Submit agent intake

These interfaces are contract drafts for agent discovery and integration planning. The static website does not expose live booking endpoints today.