everlane
Everlane is a San Francisco-based direct-to-consumer apparel and footwear brand known for its "Radical Transparency" model — publishing factory sourcing, true costs, and markups on everyday wardrobe essentials. As an API Evangelist network entry, Everlane has no bespoke public developer program; its online store runs on Shopify and exposes an agent-facing commerce surface: a live Storefront MCP server at everlane.com/api/mcp (product search, cart, product details, and policy/FAQ lookup), a Universal Commerce Protocol (UCP) profile at /.well-known/ucp, a published /llms.txt of agent instructions, and Shopify Customer Account OIDC/OAuth2 authentication. Surfaced as a Slow Ventures portfolio company and enriched from its public agent, well-known, and authentication surfaces.
More than an index entry, but the surface is still mostly links rather than artifacts — the cohort most likely to move a full band from modest, well-targeted work.
API Evangelist profiles everlane the way a machine reads it — 5 machine-readable artifacts across 1 API, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — everlane scores 21.6/100 (emerging), with a separate agent-readiness read of 25/100 (agent aware). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile everlane
Each block below is one kind of artifact we hold for everlane. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 1
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
Everlane Storefront (Shopify MCP / UCP)
Agent-facing commerce surface for the Everlane storefront, hosted on Shopify: a Model Context Protocol (MCP) server for catalog search, cart, product details, and store-policy l...
MCP Servers 1
Model Context Protocol servers expose these APIs directly to AI agents. We profile them because agent-native access is the fastest-growing way this provider's capabilities actually get used.
Model Context Protocol servers that expose these APIs to AI agents.
everlane-mcp.yml
MCP SERVERSecurity Posture 2
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Scopes 1
OAuth scopes are the vocabulary of least-privilege access. Profiling them shows exactly what an integration — or an agent acting on a user's behalf — is allowed to do.
OAuth scopes governing access to this provider's APIs.
Resources
Every other property we hold for everlane — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 1
Portal, sign-up, and the first successful call
Agent Surfaces 3
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 1
Pagination, idempotency, versioning, errors, and events
Build 1
SDKs, sample code, and the tooling you integrate with
Access & Security 3
Authentication, authorization, and security posture
Operate 1
Status, limits, changes, and where to get help
Commercial 2
Pricing, plans, and the legal terms of use
Company 1
The organization behind the API
← All providers · Data indexed from github.com/api-evangelist/everlane · machine-readable index on apis.io