EverestLabs

EverestLabs builds AI, computer vision, and robotics for the recycling industry. Its RecycleOS platform identifies, tracks, and sorts recoverable materials on the conveyor lines of Materials Recovery Facilities (MRFs), giving recyclers, reclaimers, and packaging manufacturers analytics, bale tracing, plant optimization, and compliance reporting to increase recovery rates, cut labor costs, and verify recycled content. Headquartered in Fremont, California and backed by Sierra Ventures. EverestLabs does not currently publish a public developer API, SDK, or developer portal; this profile captures its public web, GitHub, and discovery surface.

EverestLabs is profiled on the APIs.io network. Tagged areas include Company, Artificial Intelligence, Robotics, Recycling, and Computer Vision.

EverestLabs’ developer surface includes engineering blog, support, and 5 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

7.4/100 minimal ▬ flat Agent 12/100 agent aware Front door AI 5/6 prominent Heartbeat 18/100 slowing Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

0 APIs 1 MCP Servers

Company Artificial Intelligence Robotics Recycling Computer Vision Sustainability Waste Management Analytics

Kin Score

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

Composite quality — 7.4/100 · minimal

Contract Quality 0.0 / 21

Developer Ergonomics 0.0 / 17

Access Clarity 1.8 / 17

Operational Transparency 0.3 / 11

Contract Governance 0.0 / 10

Discoverability 5.0 / 9

Regulatory Posture 1.4 / 15

Agent readiness — 12/100 · agent aware

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 12 / 12

Machine-Readable Auth 0 / 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 0 / 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 0 / 6

Protected Resource Metadata 5 / 5

Registration Without a Human 0 / 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
Machine-Readable Auth +10 pts — a served openid-configuration/oauth-authorization-server carrying `issuer` (served), else the securitySchemes class in the provider OpenAPIs — bound (mutualTLS, DPoP, Signature) > negotiable (oauth2 authorizationCode/clientCredentials, openIdConnect) > bearer (http bearer, apiKey)
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)
Stable Error Semantics +8 pts — OpenAPI $refs a shared error schema across 4xx/5xx responses (verified), else common[].type includes ErrorCatalog / ErrorCodes / DeclineCodes
A2A Agent Card +8 pts — a2a/-a2a.yml manifest, graded on conformance.grade against A2A 1.0.0

11 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 0.0 — up to +20.0 composite points
Access Clarity scores 10.5 — up to +17.9 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/everestlabs/apis.yml: open an issue on github.com/api-evangelist/everestlabs 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
Electronics Recycling & Circular Economy (e-waste, battery value chain, circularity networks)	undeclared	-	-	-
ONVIF Video Analytics (video surveillance, edge cameras, computer vision)	undeclared	-	-	-

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 2

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)

MCP Servers 1

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

EverestLabs MCP Server

Remote MCP server at mcp.everestlabs.ai over HTTP.

MCP SERVER

Security Posture 1

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

Everestlabs Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Resources

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

LLMsTxt

LLMsTxt

Build 1

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

GitHubOrganization

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Operate 1

Status, limits, changes, and where to get help

Support

Support

Commercial 1

Pricing, plans, and the legal terms of use

PrivacyPolicy

PrivacyPolicy

Company 2

The organization behind the API

Website

Website

Blog

Blog

Source (apis.yml)

aid: everestlabs
name: EverestLabs
description: EverestLabs builds AI, computer vision, and robotics for the recycling industry. Its RecycleOS platform identifies,
  tracks, and sorts recoverable materials on the conveyor lines of Materials Recovery Facilities (MRFs), giving recyclers,
  reclaimers, and packaging manufacturers analytics, bale tracing, plant optimization, and compliance reporting to increase
  recovery rates, cut labor costs, and verify recycled content. Headquartered in Fremont, California and backed by Sierra
  Ventures. EverestLabs does not currently publish a public developer API, SDK, or developer portal; this profile captures
  its public web, GitHub, and discovery surface.
url: https://raw.githubusercontent.com/api-evangelist/everestlabs/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- sierra-ventures
x-tier: stub
x-tier-reason: portfolio-lead
deliveryModel:
  model: unknown
  open_source: false
  commercial: false
  callable_host: false
  label: Delivery model not determined — needs a product licence on record
  confidence: low
  source:
  - none
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source:
  - security
  generated: '2026-11-06'
  method: derived
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-19'
image: http://static1.squarespace.com/static/63bda5893fe1997b791a588a/t/658b448a5f037266c35eda3b/1703625870214/logo-black-pading.png?format=1500w
tags:
- Company
- Artificial Intelligence
- Robotics
- Recycling
- Computer Vision
- Sustainability
- Waste Management
- Analytics
tags_raw:
- Company
- Artificial Intelligence
- Robotics
- Recycling
- Computer Vision
- Sustainability
- Waste Management
- Analytics
- Computer-Vision
apis: []
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: Website
  url: https://www.everestlabs.ai/
- type: Blog
  url: https://www.everestlabs.ai/insights
- type: Support
  url: https://www.everestlabs.ai/contact
- type: PrivacyPolicy
  url: https://www.everestlabs.ai/privacy-policy
- type: GitHubOrganization
  url: https://github.com/everestlabsai
- type: LLMsTxt
  url: llms/everestlabs-llms.txt
- type: DomainSecurity
  url: security/everestlabs-domain-security.yml
x-enrichment:
  date: '2026-07-19'
  status: backfilled
  pass: local-v1
  note: backfilled from .gitignore signal + verified work evidence

EverestLabs API — frequently asked questions

Does EverestLabs have an MCP server?

Yes. APIs.io lists 1 MCP server for EverestLabs: Remote MCP server at mcp.everestlabs.ai over HTTP.

Is the EverestLabs API ready for AI agents?

EverestLabs has a Kin Score of 7.4 out of 100 (minimal) and an Agent Readiness score of 12.2 (agent-aware), scored by API Evangelist on 2026-11-06.

Work with this as data

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MCP server

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

https://apis.io/mcp

Tools for providers

9 MCP tools reach this

find_providersBrowse and filter every provider in the catalog.
get_provider_artifactsEvery artifact this provider publishes, grouped by type.
get_provider_operationsEvery operation across all of their OpenAPIs — one call instead of parsing every spec.
get_provider_toolsEvery MCP tool they ship, with the operation each wraps.
get_provider_evidenceHow each part of their score was established. Free — the basis for a claim should not sit behind it.
get_provider_ratingPRO — composite, band, trend and facet scores.
apis_io_searchSTART HERE — APIs, providers and tags for one query, each with its total.
resolveTurn a domain, URL or GitHub org into the provider it belongs to.
find_cohortsEvery scored population of providers in the catalog.

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