Napkin Math
Napkin Math is a personalized AI food journal from Paper Star (a Y Combinator company, founded 2026 in San Francisco by Manuel Castro, Claire Nord, and Jynnie Tang). Marketed as "an AI nutrition coach in your pocket," it is a simple, beautiful food diary that helps people log meals and understand their eating habits over time. The product is a consumer mobile application rather than a developer platform: as of this profile Napkin Math publishes no public API, developer documentation, SDKs, OpenAPI, MCP server, or /.well-known discovery surface. This entry is maintained in the API Evangelist network as a company profile and enrichment lead; it will be upgraded to carry API artifacts if and when Napkin Math or Paper Star exposes a public API surface.
Index entry only — little beyond a description and a link, and nothing machine-readable enough for an agent to act on without a human reading the site first.
API Evangelist profiles Napkin Math the way a machine reads it — 1 machine-readable artifact, 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 — Napkin Math scores 13.9/100 (minimal), with a separate agent-readiness read of 0/100 (human only). 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 Napkin Math
Each block below is one kind of artifact we hold for Napkin Math. 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.
Security Posture 1
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.
Resources
Every other property we hold for Napkin Math — 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.
Access & Security 1
Authentication, authorization, and security posture
Commercial 2
Pricing, plans, and the legal terms of use
Company 1
The organization behind the API
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