Price Lab
Price Lab (Price Lab Solutions) is a Latin American retail pricing intelligence platform that helps retailers and e-commerce merchants monitor competitor prices, analyze pricing KPIs, and apply AI-driven price optimization across their catalog and store network. The platform pairs competitor price scraping and competitive pricing policies with a recommendation engine, bulk data ingestion (sales, stock, replenishment, offers, and competitor prices), and electronic shelf label (ESL) management with flash strategies. Its production REST API, documented at price-lab.readme.io and hosted at backend.pricelab.com.pe, exposes product and category master data, batch price and cost updates, competitor price exports, recommendation accept/reject flows, and store-level price management, all secured with JWT bearer authentication.
Limited machine-readable signal and partial portal coverage — documentation a human can read, but little a machine or agent can consume without scraping.
API Evangelist profiles Price Lab the way a machine reads it — 13 machine-readable artifacts across 9 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 — Price Lab scores 41.4/100 (thin), with a separate agent-readiness read of 69/100 (agent native). 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 Price Lab
Each block below is one kind of artifact we hold for Price Lab. 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 9
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.
Price Lab Authentication API
The Authentication API from Price Lab — 2 operation(s) for authentication.
Price Lab Categories API
The Categories API from Price Lab — 2 operation(s) for categories.
Price Lab Competitor Pricing API
The Competitor Pricing API from Price Lab — 4 operation(s) for competitor pricing.
Price Lab Data Import API
The Data Import API from Price Lab — 1 operation(s) for data import.
Price Lab Electronic Price Tags API
The Electronic Price Tags API from Price Lab — 6 operation(s) for electronic price tags.
Price Lab Price Management API
The Price Management API from Price Lab — 5 operation(s) for price management.
Price Lab Products API
The Products API from Price Lab — 6 operation(s) for products.
Price Lab Recommendations API
The Recommendations API from Price Lab — 3 operation(s) for recommendations.
Price Lab Users API
The Users API from Price Lab — 1 operation(s) for users.
Scroll within the panel for all 9 ·
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.
price-lab-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.
Agentic Access 1
An x-agentic-access contract marks which operations are safe for an agent to run on its own and which need a human in the loop. It is the difference between an API an agent can use and one it can use safely.
Recommended x-agentic-access execution contracts for AI agents.
Resources
Every other property we hold for Price Lab — 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 2
Portal, sign-up, and the first successful call
Documentation 2
Reference material describing how the API behaves
Agent Surfaces 5
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 5
Pagination, idempotency, versioning, errors, and events
Access & Security 2
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
Other 1
Properties that don't map to a standard resource type
← All providers · Data indexed from github.com/api-evangelist/price-lab · machine-readable index on apis.io