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Garner

Garner Health is a healthcare technology company that helps members find high-quality, in-network doctors while helping employers, advisors, health plans, and providers improve care quality and lower costs. Garner analyzes more than 60 billion de-identified medical records from 320M+ patients and applies 550+ specialty-specific quality and efficiency metrics across 80+ specialties to identify Top Providers, delivered as an employer-funded benefit layered on top of existing insurance and as the Garner DataPro provider-recommendation data service. The Garner Health API (v1.x, OpenAPI 3.0.3) exposes ranked provider search, professional and facility directory detail, and provider-record annotation, authenticated with OAuth 2.0 client-credentials tokens.

agent aware

Limited machine-readable signal and partial portal coverage — documentation a human can read, but little a machine or agent can consume without scraping.

Kin Score

API Evangelist profiles Garner the way a machine reads it — 7 machine-readable artifacts across 4 APIs, 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 — Garner scores 44.9/100 (thin), with a separate agent-readiness read of 43/100 (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 Kin Score How this is scored →
scored 2026-07-27 · rubric v0.5
Composite quality — 44.9/100 · thin
Contract Quality 14.3 / 25
Developer Ergonomics 11.3 / 20
Commercial Clarity 5.8 / 20
Operational Transparency 0.0 / 13
Governance 0.0 / 12
Discoverability 10.0 / 10
Regulatory · Health 9.8 / 15
Agent readiness — 43/100 · agent aware
Machine-Readable Contract 18 / 18
Agentic Access Contract 0 / 15
MCP Server 12 / 12
Machine-Readable Auth 10 / 10
Idempotency 0 / 9
Stable Error Semantics 0 / 8
Request/Response Examples 0 / 7
Rate-Limit Signaling 0 / 7
Typed Event Surface 0 / 6
Agent Skills 5 / 5
Well-Known Catalog 0 / 4
Consent & Bot Identity 0 / 3

How we profile Garner

Each block below is one kind of artifact we hold for Garner. 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 4

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.

Garner Facilities API

The Facilities API from Garner — 1 operation(s) for facilities.

Garner Professionals API

The Professionals API from Garner — 1 operation(s) for professionals.

Garner Provider Annotations API

The Provider Annotations API from Garner — 1 operation(s) for provider annotations.

Garner Providers API

The Providers API from Garner — 1 operation(s) for providers.

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.

garner-mcp.yml

MCP SERVER

Security 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.

Garner Authentication

oauth2/http · 2 schemes

SECURITY

Garner Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Resources

Every other property we hold for Garner — 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

Documentation 2

Reference material describing how the API behaves

Agent Surfaces 3

MCP servers, agent skills, and machine-readable catalogs

Design & Contract 2

Pagination, idempotency, versioning, errors, and events

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 2

The organization behind the API

← All providers · Data indexed from github.com/api-evangelist/garner · machine-readable index on apis.io