Laurence

Laurence is a New York City based Y Combinator (Winter 2026) company that automates Amazon advertising and marketplace management for brands. The platform applies quantitative-trading methodology — hierarchical Bayesian bid optimization over Amazon Marketing Stream data landed in ClickHouse — to maximize contribution margin after margin and ad spend rather than optimizing to rules-of-thumb ACOS targets. Laurence ingests Amazon Advertising and Seller Central data, prices every keyword auction, and exposes the resulting data to customers through Ask Laurence and a hosted, OAuth-protected Model Context Protocol server that brings Amazon Ads data into Claude Code, Cursor, and Codex. The company raised a $5.8M seed round led by Susa Ventures and Box Group with participation from Y Combinator.

Laurence is listed with 1 API on the APIs.io network. Tagged areas include Company, Advertising, Amazon, E-Commerce, and Marketing.

Laurence’s developer surface includes authentication, documentation, engineering blog, signup flow, support, and 22 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

25.4/100 emerging ▬ flat Agent 34/100 agent ready Front door AI 3/6 light Heartbeat 96/100 alive saas Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

AccessSelf serve

1 API 1 MCP Servers 3 Agent Skills

Company Advertising Amazon E-Commerce Marketing Machine Learning Retail Media MCP

Kin Score

Kin Score How this is scored →
scored 2026-11-06 · rubric v0.27.0

Composite quality — 25.4/100 · emerging

Contract Quality 0.0 / 21

Developer Ergonomics 5.2 / 17

Access Clarity 5.8 / 17

Operational Transparency 0.3 / 11

Contract Governance 1.9 / 10

Discoverability 6.4 / 9

Regulatory Posture 4.8 / 15

Agent readiness — 34/100 · agent ready

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 12 / 12

Machine-Readable Auth 10 / 10

Idempotency 0 / 9

Stable Error Semantics 8 / 8

Request/Response Examples 0 / 7

Rate-Limit Signaling 7 / 7

Typed Event Surface 0 / 6

Agent Skills 5 / 5

Well-Known Catalog 0 / 4

Consent & Bot Identity 0 / 3

A2A Agent Card 0 / 8

Dry-Run / Simulate Mode 0 / 4

Delegated User Identity 6 / 6

Protected Resource Metadata 5 / 5

Registration Without a Human 6 / 6

Agentic Commerce Well-Known Document 0 / 5

Pagination 5 / 5

Distinct Operations 3 / 3

MCP Tool Quality 6 / 6

Regulatory Posture applies to this provider. Its tags matched the Horizontal (data, software, accessibility, platform) regime, so Regulatory Posture carries 15 points of the composite. If this regime is wrong for your business, say so on your provider repo — the applicability map is public and we will correct it.

Create-or-Update Ergonomics could not be measured. We hold no machine-readable contract for this provider to read, so there is nothing to measure a write surface against. Excluded rather than scored zero: never-measured and measured-empty are different facts. Publishing an OpenAPI is what makes this facet — and several others — scorable at all.

The six quality facets above are damped to 85 points between them, because the conditional facet above carries the other 15. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 85% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.

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Agent readiness — checks not met, largest first (exact, from this profile):

Machine-Readable Contract +18 pts — api_specs includes an OpenAPI document
Agentic Access Contract +10 pts — agentic_access artifact present (x-agentic-access), graded by author
Idempotency +9 pts — OpenAPI mutating operations declare an idempotency key — a header, a request-body property, or a named de-duplication token whose own description says so (verified); else common[].type matches /idempoten/i (documented)
A2A Agent Card +8 pts — a2a/-a2a.yml manifest, graded on conformance.grade against A2A 1.0.0
Request/Response Examples +7 pts — share of operations carrying request/response examples; else an Examples / Code Samples link
Typed Event Surface +6 pts — AsyncAPI document, OR webhooks/callbacks objects in the as-published OpenAPI, OR common[].type includes Webhooks

5 more unmet checks in the Agent Readiness table above.

Kin Score — facets with the most composite points still available (weight × headroom):

Contract Quality scores 0.0 — up to +25.0 composite points
Developer Ergonomics scores 30.4 — up to +13.9 composite points
Access Clarity scores 34.2 — up to +13.2 composite points

Each check links to what satisfies it. Publish the artifact, or tell us where it already lives, and the score moves on the next scoring run.

Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from the provider profile at artifacts.apis.io/laurence/apis.yml: open an issue on github.com/api-evangelist/laurence to ask a question or point us at an artifact you publish. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

Markets this provider is in provisional

The Kin Score Market layer reads a provider inside its market: its position, and how deep its integrations go. Depth is only comparable within a position. Provisional and unweighted: not part of the Kin Score or agent readiness.

Market	Position	Depth	Registered MCP server	Declares compatibility
Amazon Seller Ecosystem (Selling Partner Appstore, Amazon Ads partners)	builds-on	17.2 (thin)	-	1

Registered MCP server and declares compatibility are report only — not weighted. Undeclared means we found nothing we can read, so there is no number: we never infer integrations.

Standards implemented 3

Standards this provider implements or carries. De facto ones are profiled and measured by the API Commons; formal ones come from the standards directory and are linked by the provider's own tags. The evidence column says how each claim was established — not that it was made.

Model Context Protocol (MCP)

measured — observed by APIs.io (verified)

OAuth 2.0 Protected Resource Metadata

measured — observed by APIs.io (verified)

Dynamic Client Registration

measured — observed by APIs.io (published)

APIs 1

Individual APIs this provider publishes, each with its own machine-readable definition.

Laurence MCP

Laurence MCP is a hosted, remote Model Context Protocol server that exposes a read-only set of nine tools over a customer's Amazon Advertising and Amazon Marketing Stream data —...

MCP Servers 1

Model Context Protocol servers that expose these APIs to AI agents.

Laurence MCP Server

Laurence operates an official hosted, remote MCP server that brings a customer's Amazon Advertising and Amazon Marketing Stream (AMS) data into Claude Code, Cursor, and Codex. T...

MCP SERVER

Agent Skills 3

Packaged agent skills for driving this provider's APIs from an AI assistant.

Connect to Laurence MCP and resolve your Amazon Ads scope

AGENT SKILL

Find wasted Amazon Ads spend from search terms and negatives

AGENT SKILL

Diagnose hourly Amazon Marketing Stream performance and bid moves

AGENT SKILL

Laurence API pricing

Pricing Plans 1

Published pricing tiers and plan structures.

Laurence Plans Pricing

0 plans

PLANS

Rate Limits 1

Documented rate limits and quota policies.

Laurence Rate Limits

0 limits

RATE LIMITS

Security Posture 2

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Laurence Authentication

1 scheme

SECURITY

Laurence Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Scopes 1

OAuth scopes governing access to this provider's APIs.

Laurence Scopes

4 scopes

SCOPES

Resources

Get Started 2

Portal, sign-up, and the first successful call

SignUp

SignUp

Login

Documentation 1

Reference material describing how the API behaves

Documentation

Documentation

Agent Surfaces 4

MCP servers, agent skills, and machine-readable catalogs

WellKnown

LLMsTxt

MCPServer

AgentSkill

Design & Contract 4

Pagination, idempotency, versioning, errors, and events

Conformance

Conventions

ErrorCatalog

Lifecycle

Build 2

SDKs, sample code, and the tooling you integrate with

Packages

Packages

GitHubOrganization

GitHubOrganization

Access & Security 3

Authentication, authorization, and security posture

Authentication

Authentication

OAuthScopes

OAuthScopes

DomainSecurity

DomainSecurity

Operate 2

Status, limits, changes, and where to get help

RateLimits

RateLimits

Support

Support

Commercial 3

Pricing, plans, and the legal terms of use

Plans

Plans

TermsOfService

TermsOfService

PrivacyPolicy

PrivacyPolicy

Company 5

The organization behind the API

Website

Website

Blog

Blog

BlogRSS

BlogRSS

LinkedIn

Twitter

Twitter

Other 1

Properties that don't map to a standard resource type

Sitemap

Sitemap

Source (apis.yml)

aid: laurence
name: Laurence
description: Laurence is a New York City based Y Combinator (Winter 2026) company that automates Amazon advertising and marketplace
  management for brands. The platform applies quantitative-trading methodology — hierarchical Bayesian bid optimization over
  Amazon Marketing Stream data landed in ClickHouse — to maximize contribution margin after margin and ad spend rather than
  optimizing to rules-of-thumb ACOS targets. Laurence ingests Amazon Advertising and Seller Central data, prices every keyword
  auction, and exposes the resulting data to customers through Ask Laurence and a hosted, OAuth-protected Model Context Protocol
  server that brings Amazon Ads data into Claude Code, Cursor, and Codex. The company raised a $5.8M seed round led by Susa
  Ventures and Box Group with participation from Y Combinator.
deliveryModel:
  model: saas
  open_source: false
  commercial: true
  callable_host: false
  label: Hosted service · you call their endpoint
  confidence: medium
  source:
  - pricing
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: unknown
  onboarding: self-serve
  trial: false
  try_now: false
  public: false
  gated: none
  gated_source:
  - onboarding
  label: Self-serve signup
  confidence: medium
  source:
  - authentication
  - scopes
  - rate-limits
  - security
  generated: '2026-11-06'
  method: derived
image: https://www.laurence.com/icon.png
url: https://raw.githubusercontent.com/api-evangelist/laurence/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- y-combinator
- susa-ventures
- box-group
x-tier: profiled
x-tier-reason: hosted-mcp-server
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-08-13'
tags:
- Company
- Advertising
- Amazon
- E-Commerce
- Marketing
- Machine Learning
- Retail Media
- MCP
tags_raw:
- Company
- Advertising
- Amazon
- E-Commerce
- Marketing
- Machine Learning
- Retail Media
- MCP
- Machine-Learning
apis:
- aid: laurence:mcp
  name: Laurence MCP
  description: Laurence MCP is a hosted, remote Model Context Protocol server that exposes a read-only set of nine tools over
    a customer's Amazon Advertising and Amazon Marketing Stream data — allowed ads profiles, daily sales, campaigns, ad groups,
    keywords, negative keywords, bid observations, hourly and daily stream metrics, and search term reports. It is available
    to Laurence customers, is authenticated with OAuth 2.0 (RFC 9728 protected resource, PKCE S256, dynamic client registration),
    and every tool call is scoped to the Amazon Ads profiles the signed-in user belongs to. The server's RFC 9728 protected-resource
    metadata names https://mcp.laurence.com/mcp as the canonical resource as of 2026-08-13; the launch post still publishes
    the original Modal deployment URL, which remains live and behaves identically.
  humanURL: https://www.laurence.com/blog/laurence-mcp-launch
  baseURL: https://mcp.laurence.com/mcp
  tags:
  - MCP
  - Amazon Ads
  - Advertising
  - Reporting
  properties:
  - type: MCPServer
    url: mcp/laurence-mcp.yml
  - type: Authentication
    url: authentication/laurence-authentication.yml
  - type: OAuthScopes
    url: scopes/laurence-scopes.yml
  - type: Documentation
    url: https://www.laurence.com/blog/laurence-mcp-launch
  - type: AgentSkill
    url: skills/_index.yml
  - type: ErrorCatalog
    url: errors/laurence-problem-types.yml
  - type: RateLimits
    url: rate-limits/laurence-rate-limits.yml
common:
- type: WellKnown
  url: well-known/laurence-well-known.yml
- type: LLMsTxt
  url: llms/laurence-llms.txt
- type: MCPServer
  url: mcp/laurence-mcp.yml
- type: Authentication
  url: authentication/laurence-authentication.yml
- type: OAuthScopes
  url: scopes/laurence-scopes.yml
- type: Conformance
  url: conformance/laurence-conformance.yml
- type: Conventions
  url: conventions/laurence-conventions.yml
- type: DomainSecurity
  url: security/laurence-domain-security.yml
- type: Packages
  url: packages/laurence-packages.yml
- type: Plans
  url: plans/laurence-plans-pricing.yml
- type: RateLimits
  url: rate-limits/laurence-rate-limits.yml
- type: ErrorCatalog
  url: errors/laurence-problem-types.yml
- type: Lifecycle
  url: lifecycle/laurence-lifecycle.yml
- type: AgentSkill
  url: skills/_index.yml
- type: Website
  url: https://www.laurence.com
- type: Documentation
  url: https://www.laurence.com/how-it-works
- type: Blog
  url: https://www.laurence.com/blog
- type: BlogRSS
  url: https://www.laurence.com/blog/feed.xml
- type: SignUp
  url: https://www.laurence.com/audit
- type: TermsOfService
  url: https://www.laurence.com/eula
- type: PrivacyPolicy
  url: https://www.laurence.com/privacy
- type: Support
  url: https://www.laurence.com/demo
- type: Login
  url: https://www.laurence.com/auth/signin
- type: GitHubOrganization
  url: https://github.com/Laurence-AI
- type: Sitemap
  url: https://www.laurence.com/sitemap.xml
- type: LinkedIn
  url: https://www.linkedin.com/company/laurence-ai
- type: Twitter
  url: https://x.com/trylaurence
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
x-enrichment:
  date: '2026-08-13'
  status: enriched
  artifacts_added: 9
  pass: local-v1

Laurence API — frequently asked questions

Does Laurence have an API?

Yes. APIs.io lists 1 API from Laurence, including Laurence MCP.

How do I authenticate with the Laurence API?

Laurence documents its authentication (1 scheme). The details are profiled on APIs.io as Laurence Authentication.

Does Laurence have an MCP server?

Yes. APIs.io lists 1 MCP server for Laurence: Laurence operates an official hosted, remote MCP server that brings a customer's Amazon Advertising and Amazon Marketing Stream (AMS) data into Claude Code, Cursor, and Codex. The server is available

Is the Laurence API ready for AI agents?

Laurence has a Kin Score of 25.4 out of 100 (emerging) and an Agent Readiness score of 33.6 (agent-ready), scored by API Evangelist on 2026-11-06.

Work with this as data

Every provider here is available over the APIs.io API and to AI agents over MCP.

MCP server

One button, every client — Claude, Cursor, VS Code and the rest.

https://apis.io/mcp

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