Monte Carlo website screenshot

Monte Carlo

Monte Carlo is a data and AI observability platform that monitors data warehouses, lakes, and pipelines for freshness, volume, schema, and quality anomalies, helping data teams detect, resolve, and prevent data downtime across Snowflake, Databricks, BigQuery, Redshift, and other modern data stack tools. Monte Carlo exposes a GraphQL API at https://api.getmontecarlo.com/graphql used for programmatic access to monitors, incidents, lineage, assets, alerts, custom rules, and lake/metastore integrations, with a supporting Python SDK and CLI (pycarlo / montecarlo). Authentication uses an API Key ID and Token pair sent via headers.

Monte Carlo publishes 1 API on the APIs.io network: Graphql API. Tagged areas include Data Observability, Data Quality, Data Reliability, Data Lake, and Data Warehouse.

Monte Carlo’s developer surface includes authentication, documentation, pricing, signup flow, engineering blog, and 7 more developer resources.

🌐 Visit website 📡 Source on GitHub
31.9/100 thin ▬ flat Agent 34/100 agent aware Full breakdown ↓
scored 2026-07-28 · rubric v0.6
AccessSelf serve
2 APIs
Data ObservabilityData QualityData ReliabilityData LakeData WarehouseLineageMonitoringAI Observability

Kin Score

Kin Score Kin Score How this is scored →
scored 2026-07-28 · rubric v0.6
Composite quality — 31.9/100 · thin
Contract Quality 16.3 / 25
Developer Ergonomics 4.3 / 20
Commercial Clarity 3.7 / 20
Operational Transparency 0.7 / 13
Governance 0.0 / 12
Discoverability 6.9 / 10
Agent readiness — 34/100 · agent aware
Machine-Readable Contract 18 / 18
Agentic Access Contract 10 / 10
MCP Server 0 / 12
Machine-Readable Auth 10 / 10
Idempotency 0 / 9
Stable Error Semantics 0 / 8
Request/Response Examples 7 / 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
Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. This rating is computed from github.com/api-evangelist/monte-carlo: open an issue to ask a question, or submit a pull request to add artifacts. Want it done for you? Prioritized profiling — $2,500 →

APIs 2

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

Monte Carlo GraphQL API

GraphQL API for the Monte Carlo data observability platform. Provides programmatic access to monitors, incidents, assets, lineage, custom rules, warehouses, lakes, metastores, a...

Monte Carlo Graphql API

The Graphql API from Monte Carlo — 1 operation(s) for graphql.

Open Collections 1

Open, tool-agnostic API collections (OpenAPI-derived and Bruno).

Monte Carlo GraphQL API

OPEN COLLECTION

GraphQL 1

GraphQL schemas published by this provider.

Monte Carlo GraphQL API

GraphQL API for the Monte Carlo data observability platform. Provides programmatic access to monitors, incidents, assets, lineage, custom rules, warehouses, lakes, metastores, a...

GRAPHQL

Security Posture 3

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

Monte Carlo Authentication

apiKey · 1 scheme

SECURITY

Monte Carlo Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Monte Carlo Trust Center

SOC 2, ISO 27001

SECURITY

Agentic Access 1

Recommended x-agentic-access execution contracts for AI agents.

Monte Carlo Agentic Access

1 operation · 1 acting

1 operations · 1 acting

AGENTIC

Resources

Get Started 1

Portal, sign-up, and the first successful call

Signup
Signup

Documentation 1

Reference material describing how the API behaves

Documentation
Documentation

Agent Surfaces 2

MCP servers, agent skills, and machine-readable catalogs

AgenticAccess
AgenticAccess
 LlmsText
LlmsText

Build 1

SDKs, sample code, and the tooling you integrate with

GitHubOrganization
GitHubOrganization

Access & Security 3

Authentication, authorization, and security posture

TrustCenter
TrustCenter
 DomainSecurity
DomainSecurity
 Authentication
Authentication

Commercial 1

Pricing, plans, and the legal terms of use

Pricing
Pricing

Company 3

The organization behind the API

LinkedIn
LinkedIn
 Website
Website
 Blog
Blog

Source (apis.yml)

apis.yml Raw ↑ 
aid: monte-carlo
name: Monte Carlo
description: Monte Carlo is a data and AI observability platform that monitors data warehouses, lakes, and pipelines for freshness,
  volume, schema, and quality anomalies, helping data teams detect, resolve, and prevent data downtime across Snowflake, Databricks,
  BigQuery, Redshift, and other modern data stack tools. Monte Carlo exposes a GraphQL API at https://api.getmontecarlo.com/graphql
  used for programmatic access to monitors, incidents, lineage, assets, alerts, custom rules, and lake/metastore integrations,
  with a supporting Python SDK and CLI (pycarlo / montecarlo). Authentication uses an API Key ID and Token pair sent via headers.
type: Index
accessModel:
  pricing: unknown
  onboarding: self-serve
  trial: false
  try_now: false
  public: false
  label: Self-serve signup
  confidence: medium
  source:
  - authentication
  generated: '2026-07-22'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/monte-carlo.png
tags:
- Data Observability
- Data Quality
- Data Reliability
- Data Lake
- Data Warehouse
- Lineage
- Monitoring
- AI Observability
url: https://raw.githubusercontent.com/api-evangelist/monte-carlo/refs/heads/main/apis.yml
created: '2026-05-11'
modified: '2026-05-11'
specificationVersion: '0.19'
apis:
- aid: monte-carlo:graphql-api
  name: Monte Carlo GraphQL API
  description: GraphQL API for the Monte Carlo data observability platform. Provides programmatic access to monitors, incidents,
    assets, lineage, custom rules, warehouses, lakes, metastores, and alerts. Authentication uses an account or personal API
    Key ID and Token sent via the x-mcd-id and x-mcd-token headers (or as a Bearer token).
  humanURL: https://docs.getmontecarlo.com/docs/api
  baseURL: https://api.getmontecarlo.com/graphql
  tags:
  - Data Observability
  - GraphQL
  - Monitoring
  - Data Quality
  - Lineage
  properties:
  - type: Documentation
    url: https://docs.getmontecarlo.com/docs/api
  - type: Developer Resources
    url: https://docs.getmontecarlo.com/docs/developer-resources
  - type: Data Lakes Integration
    url: https://docs.getmontecarlo.com/docs/data-lakes
  - type: Python SDK (pycarlo)
    url: https://github.com/monte-carlo-data/python-sdk
  - type: CLI
    url: https://github.com/monte-carlo-data/monte-carlo-cli
  - type: MCP Server
    url: https://docs.getmontecarlo.com/docs/mcp-server
  - url: graphql/monte-carlo-graphql.md
    type: GraphQL
- aid: monte-carlo:monte-carlo-graphql-api
  name: Monte Carlo Graphql API
  description: The Graphql API from Monte Carlo — 1 operation(s) for graphql.
  humanURL: https://docs.getmontecarlo.com/docs/api
  baseURL: https://api.getmontecarlo.com/graphql
  tags:
  - Graphql
  properties:
  - type: OpenAPI
    url: openapi/monte-carlo-graphql-api-openapi.yml
common:
- type: AgenticAccess
  url: agentic-access/monte-carlo-agentic-access.yml
- type: TrustCenter
  url: security/monte-carlo-trust-center.yml
- type: DomainSecurity
  url: security/monte-carlo-domain-security.yml
- type: Authentication
  url: authentication/monte-carlo-authentication.yml
- type: LinkedIn
  url: https://www.linkedin.com/company/monte-carlo-data
- type: Website
  url: https://www.montecarlodata.com
- type: Documentation
  url: https://docs.getmontecarlo.com
- type: GitHubOrganization
  url: https://github.com/monte-carlo-data
- type: Pricing
  url: https://www.montecarlodata.com/pricing/
- type: Signup
  url: https://www.montecarlodata.com/request-a-demo/
- type: LlmsText
  url: https://docs.getmontecarlo.com/llms.txt
- url: https://montecarlodata.com/blog/feed/
  type: Blog
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com