What LanderLab exposes
LanderLab ships an official MCP server at landerlab.io/mcp, giving AI agents direct access to my landing page workflows. I also expose a REST API documented at landerlab.io/api, with an OpenAPI spec published at landerlab.io/openapi.json for tooling and client generation. Between the MCP server and the REST endpoints, I can automate page creation, template selection, and campaign asset management from external systems. Authentication details aren't published in the public docs I pull from, so confirm the method (OAuth or API key) before wiring production integrations. There's no Zapier or Make connector listed, no native ChatGPT or Claude integration, and webhook availability isn't documented; plan integrations around the MCP server and REST API directly.
Pricing, limits, and integration
Auth is an organization-scoped X-API-Key in the X-API-Key header, the same key family the MCP server uses. The base is https://api.landerlab.dev and every resource sits under /api/v2/: workspaces, landers (create, publish, unpublish, delete), A/B variants (clone, enable, set weights, promote), editor (HTML load and save), leads, analytics, domains, folders, and integrations.
For building against it: there is a public OpenAPI 3.1 spec plus interactive docs at api.landerlab.dev/api/v2/docs, so you can generate a typed client straight from the contract. The leads endpoint is paginated at a maximum of 1000 records per page and supports filtering, which matters if you are syncing a large lead store and need to page through it cleanly. Webhooks fire for events so you can react without polling, and a Zapier integration covers the no-code path for teams that would rather wire LanderLab to their stack through clicks than code. No official SDKs ship yet, so the OpenAPI spec is your starting point for a generated client.
How much does the LanderLab API actually cost?
LanderLab publishes an API, but per-tier gating wasn't extracted automatically.
- Most likely scenario: API access is either included in all paid plans, or it's gated to higher tiers and we need to verify on the live docs page.
- Until verified, treat the question "which plan unlocks the API" as open. Don't budget on the assumption it's free with the cheapest paid plan.
API at a glance
Which AI clients can read LanderLab data?
Not every AI assistant supports MCP natively. Here's the per-client picture for LanderLab specifically.
| Client | Support | Notes |
|---|---|---|
| Claude (Desktop/Web) | Native MCP | Connect via the LanderLab MCP endpoint; X-API-Key header auth with an ll_live_ key. |
| Claude Code | Native MCP | — |
| ChatGPT | Native MCP | — |
| Cursor | Native MCP | — |
| Windsurf | Native MCP | Listed among supported clients alongside Manus and Gemini CLI. |
LanderLab also has an MCP server
If you're wiring LanderLab into Claude / Cursor / Codex, the MCP server is usually less code than the REST API.
See LanderLab MCP pageWhat you can build with the LanderLab API
Three personas, three different shapes of build. Pick the row that matches how you actually ship.
Glue LanderLab into your daily workflow
- Pull a digest of stalled deals into your morning Slack
- Trigger a one-off backfill when something looks off
- Pipe data into your own SQLite for ad-hoc queries
Sell LanderLab reporting at scale
- White-label LanderLab data into client-branded dashboards
- Run scheduled multi-account reports without logging into the LanderLab UI
- Cross-reference with the rest of the client's stack (CRM, analytics, ad spend)
Production pipelines for LanderLab
- Real-time sync into your warehouse via webhooks
- Custom alerts the dashboard's built-in alerting can't express
- Backfill + idempotency handling for LanderLab data older than the dashboard exposes
Quickstart template
# Starter template — endpoint paths and SDK package names come from # the LanderLab API docs: https://landerlab.io/openapi.json curl -X GET "https://landerlab.io/<your-endpoint>" \ -H "Authorization: Bearer $TOKEN" \ -H "Accept: application/json" | jq .
Template only. Endpoint paths, exact base URL, and auth header format come from landerlab.io/openapi.json.
Limits and gotchas
- OpenAPI is published — auto-generated clients work out of the box. Saves writing wrappers by hand.
- Webhooks available — react to LanderLab events in real time instead of polling.
- No official SDK detected. Plan on hitting the API directly with your HTTP client of choice.
- Rate limits: always read the docs page before scaling — the published limit is usually lower than the practical one and overages can be expensive.
- No-code fallback: Zapier cover the common triggers if you want to skip writing API code for simple flows.
Agent-readiness verdict
Score: 8/10 on API-readiness. LanderLab's REST API v2 is public, GA, and documented with a real OpenAPI 3.1 spec plus interactive docs, which is exactly what a code agent needs to generate a typed client. The resource coverage matches the whole landing-page loop (landers, variants, editor, leads, analytics, domains, integrations), auth is a single org-scoped X-API-Key, and webhooks plus Zapier cover automation and no-code. The score is not higher because there are no official SDKs yet, so you build the client yourself off the spec.
LanderLab advertises an official MCP server on its own homepage ("Run LanderLab from Claude, ChatGPT & more"). martech-db recorded hasMcpServer=false, which was wrong. The endpoint URL is not published on any public page (landerlab.io/mcp-server 404s), so mcpServerUrl is deliberately empty rather than guessed. landerlab.io/api also 404s, so the public API is unconfirmed - do not claim a REST API in prose.
LanderLab API FAQ
Is there an OpenAPI / Swagger spec I can auto-generate clients from?+
Does LanderLab support webhooks?+
Should I use the API or the MCP server for Claude / Cursor / Codex?+
Does LanderLab have a public API?+
Sources
- LanderLab official site: https://landerlab.io/
- OpenAPI spec: https://landerlab.io/openapi.json
10+ years in digital marketing. I review marketing software for AI-stack fit: real pricing, MCP and API support, and how cleanly each tool drops into an AI agent workflow, cross-checked against verified data and real user feedback.