Monte Carlo

Monte Carlo is the data + AI observability platform that detects, resolves and prevents data quality incidents across warehouses, lakes, BI tools, ETL orchestrators and, increasingly, AI agents. Its programmatic surface is a single GraphQL API at api.getmontecarlo.com/graphql that exposes everything the web app shows — alerts, incidents, monitors, assets, table and column lineage, query history and job performance — alongside a REST Push Ingest API at integrations.getmontecarlo.com for pushing metadata, lineage and query logs from sources the pull-based collectors cannot reach. Monte Carlo also runs a first-party hosted MCP server with 56 documented tools and OAuth 2.1 dynamic client registration, publishes 19 Apache-2.0 Agent Skills for coding agents, and ships a Python SDK (pycarlo), a CLI (montecarlodata) and an OpenTelemetry SDK for agent tracing. The company rebranded its web presence to montecarlo.ai during 2026; montecarlodata.com still resolves and serves the same content.

Monte Carlo publishes 3 APIs on the APIs.io network. Tagged areas include AIOps, Data Observability, Data Quality, Data Lineage, and Agent Observability.

The Monte Carlo catalog on APIs.io includes 1 event-driven AsyncAPI specification.

Monte Carlo’s developer surface includes documentation, API reference, getting-started guide, engineering blog, pricing, changelog, CLI, and 28 more developer resources.

🌐 Visit website 📡 Source on GitHub

50.5/100 developing ▬ flat Agent 51/100 agent ready saas Full breakdown ↓
scored 2026-08-30 · rubric v0.17.2

3 APIs 1 MCP Servers

AIOps Data Observability Data Quality Data LineageAgent ObservabilityMonitoring GraphQL MCP OpenTelemetry Data Engineering

Kin Score

Kin Score How this is scored →
scored 2026-08-30 · rubric v0.17.2

Composite quality — 50.5/100 · developing

Contract Quality 10.7 / 25

Developer Ergonomics 12.6 / 20

Access Clarity 11.1 / 20

Operational Transparency 5.8 / 13

Contract Governance 2.2 / 12

Discoverability 8.2 / 10

Agent readiness — 51/100 · agent ready

Machine-Readable Contract 18 / 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 6 / 6

Agent Skills 5 / 5

Well-Known Catalog 4 / 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 Surface 0 / 5

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-data: 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 3

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

MCP Servers 1

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

Rate Limits 1

Documented rate limits and quota policies.

Monte Carlo Data Rate Limits

0 limits

RATE LIMITS

FinOps 1

Cost, billing, and metering signals for API financial operations.

Scopes 1

OAuth scopes governing access to this provider's APIs.

Monte Carlo Data Scopes

OAuth 2.0 · no documented scopes

0 scopes

SCOPES

Resources

Get Started 2

Portal, sign-up, and the first successful call

DeveloperPortal

DeveloperPortal

GettingStarted

GettingStarted

Documentation 2

Reference material describing how the API behaves

Documentation

Documentation

APIReference

APIReference

Agent Surfaces 3

MCP servers, agent skills, and machine-readable catalogs

LLMsTxt

LLMsTxt

WellKnown

WellKnown

AgentSkill

AgentSkill

Design & Contract 5

Pagination, idempotency, versioning, errors, and events

Conformance

Conventions

ErrorCatalog

Lifecycle

Webhooks

Build 4

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

Packages

SDKs

CLI

Access & Security 8

Authentication, authorization, and security posture

SecurityTxt

SecurityTxt

Security

Security

VulnerabilityDisclosure

VulnerabilityDisclosure

TrustCenter

Compliance

DomainSecurity

Authentication

OAuthScopes

Scroll for all 8

Operate 4

Status, limits, changes, and where to get help

StatusPage

ChangeLog

ChangeLog

RateLimits

Commercial 4

Pricing, plans, and the legal terms of use

Pricing

TermsOfService

PrivacyPolicy

Plans

Company 3

The organization behind the API

Website

Website

Blog

Blog

LinkedIn

Source (apis.yml)

aid: monte-carlo-data
name: Monte Carlo
description: Monte Carlo is the data + AI observability platform that detects, resolves and prevents data quality incidents
  across warehouses, lakes, BI tools, ETL orchestrators and, increasingly, AI agents. Its programmatic surface is a single
  GraphQL API at api.getmontecarlo.com/graphql that exposes everything the web app shows — alerts, incidents, monitors, assets,
  table and column lineage, query history and job performance — alongside a REST Push Ingest API at integrations.getmontecarlo.com
  for pushing metadata, lineage and query logs from sources the pull-based collectors cannot reach. Monte Carlo also runs
  a first-party hosted MCP server with 56 documented tools and OAuth 2.1 dynamic client registration, publishes 19 Apache-2.0
  Agent Skills for coding agents, and ships a Python SDK (pycarlo), a CLI (montecarlodata) and an OpenTelemetry SDK for agent
  tracing. The company rebranded its web presence to montecarlo.ai during 2026; montecarlodata.com still resolves and serves
  the same content.
type: Index
deliveryModel:
  model: saas
  open_source: false
  commercial: true
  callable_host: true
  label: Commercial SaaS with a callable multi-tenant API and a hosted MCP server
  confidence: high
  source:
  - https://docs.getmontecarlo.com/docs/api
  - https://docs.getmontecarlo.com/docs/mcp-server
  - https://docs.getmontecarlo.com/docs/integration-feature-lifecycles
  generated: '2026-08-29'
  method: searched
accessModel:
  pricing: enterprise-quoted
  onboarding: sales
  trial: false
  try_now: false
  public: false
  label: Enterprise, sales-quoted
  confidence: high
  source:
  - plans
  - https://montecarlo.ai/request-for-pricing/
  generated: '2026-08-29'
  method: searched
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/monte-carlo-data.png
tags:
- AIOps
- Data Observability
- Data Quality
- Data Lineage
- Agent Observability
- Monitoring
- GraphQL
- MCP
- OpenTelemetry
- Data Engineering
tags_raw:
- AIOps
- Data Observability
- Data Quality
- Data Lineage
- Agent Observability
- Monitoring
- GraphQL
- Model Context Protocol
- OpenTelemetry
- Data Engineering
url: https://raw.githubusercontent.com/api-evangelist/monte-carlo-data/refs/heads/main/apis.yml
created: '2026-03-27'
modified: '2026-08-29'
specificationVersion: '0.23'
apis:
- aid: monte-carlo-data:monte-carlo-data
  name: Monte Carlo GraphQL API
  description: Monte Carlo's single GraphQL endpoint. Every piece of information the web application presents can be retrieved
    and mutated programmatically — alerts and incidents, monitors and monitors-as-code, assets, table and column lineage,
    query history, job and task performance, domains, authorization groups, and API/OAuth/MCP credential management. Authenticate
    with an x-mcd-id / x-mcd-token key pair or with an OAuth 2.0 client-credentials bearer token. Introspection is auth-gated,
    so no SDL is publicly downloadable; a generated HTML reference is published at apidocs.getmontecarlo.com and an in-browser
    GraphiQL explorer at getmontecarlo.com/graphiql.
  humanURL: https://docs.getmontecarlo.com/docs/api
  baseURL: https://api.getmontecarlo.com/graphql
  tags:
  - AIOps
  - GraphQL
  - Data Observability
  properties:
  - type: Documentation
    url: https://docs.getmontecarlo.com/docs/api
  - type: APIReference
    url: https://apidocs.getmontecarlo.com/
  - type: GraphQL
    url: https://api.getmontecarlo.com/graphql
  - type: Authentication
    url: authentication/monte-carlo-data-authentication.yml
  - type: OAuthScopes
    url: scopes/monte-carlo-data-scopes.yml
- aid: monte-carlo-data:push-ingest-api
  name: Monte Carlo Push Ingest API
  description: A REST write API for pushing observability data into Monte Carlo from sources its pull-based collectors cannot
    reach. Three endpoints — POST /ingest/v1/metadata (table and view schema, columns, row and byte counts, freshness), POST
    /ingest/v1/lineage (table- and column-level lineage) and POST /ingest/v1/querylogs (SQL query history) — each returning
    202 Accepted with an invocation_id. Requires a dedicated Ingestion-scoped integration key; standard API keys are rejected.
    Public preview, Enterprise plan or higher.
  humanURL: https://docs.getmontecarlo.com/docs/push-ingest-api
  baseURL: https://integrations.getmontecarlo.com
  tags:
  - Data Observability
  - Data Lineage
  - Ingest
  properties:
  - type: Documentation
    url: https://docs.getmontecarlo.com/docs/push-ingest-api
  - type: ErrorCatalog
    url: errors/monte-carlo-data-problem-types.yml
- aid: monte-carlo-data:mcp-server
  name: Monte Carlo MCP Server
  description: A first-party, fully hosted Model Context Protocol server that gives AI agents direct access to Monte Carlo
    — investigating alerts, exploring assets and lineage, creating and tuning monitors, and evaluating AI agent performance.
    56 documented tools across three toolsets (default, extended, agent_observability) plus four published MCP prompts. Reached
    over streamable HTTP with OAuth 2.1 and dynamic client registration, or with MCP-scoped keys; also shipped as an Anthropic-verified
    connector in the Claude Connectors Directory. Runs on AWS Lambda, stateless, executing under the calling user's Monte
    Carlo identity.
  humanURL: https://docs.getmontecarlo.com/docs/mcp-server
  baseURL: https://mcp.getmontecarlo.com/mcp
  tags:
  - MCP
  - Agent Observability
  - AIOps
  tags_raw:
  - Model Context Protocol
  - Agent Observability
  - AIOps
  properties:
  - type: MCPServer
    url: mcp/monte-carlo-data-mcp.yml
  - type: ToolCrosswalk
    url: mcp/monte-carlo-data-tool-crosswalk.yml
  - type: Documentation
    url: https://docs.getmontecarlo.com/docs/mcp-server
common:
- type: Website
  url: https://www.montecarlodata.com
- type: DeveloperPortal
  url: https://docs.getmontecarlo.com
- type: Documentation
  url: https://docs.getmontecarlo.com
- type: APIReference
  url: https://apidocs.getmontecarlo.com/
- type: GettingStarted
  url: https://docs.getmontecarlo.com/docs/welcome
- type: Blog
  url: https://montecarlodata.com/blog/feed/
- type: GitHubOrganization
  url: https://github.com/monte-carlo-data
- type: LinkedIn
  url: https://www.linkedin.com/company/monte-carlo-data
- type: Integrations
  url: https://www.montecarlodata.com/product/integrations
- type: Pricing
  url: https://montecarlo.ai/request-for-pricing/
- type: TermsOfService
  url: https://montecarlo.ai/terms-of-service
- type: PrivacyPolicy
  url: https://montecarlo.ai/privacy-policy
- type: StatusPage
  url: https://status.getmontecarlo.com/
- type: ChangeLog
  url: https://docs.getmontecarlo.com/changelog
- type: ChangeLog
  url: changelog/monte-carlo-data-changelog.yml
- type: LLMsTxt
  url: llms/monte-carlo-data-llms.txt
- type: Packages
  url: packages/monte-carlo-data-packages.yml
- type: SDKs
  url: packages/monte-carlo-data-packages.yml
- type: CLI
  url: cli/monte-carlo-data-cli.yml
- type: WellKnown
  url: well-known/monte-carlo-data-well-known.yml
- type: SecurityTxt
  url: well-known/monte-carlo-data-security.txt
- type: Security
  url: security/monte-carlo-data-vulnerability-disclosure.yml
- type: VulnerabilityDisclosure
  url: security/monte-carlo-data-vulnerability-disclosure.yml
- type: TrustCenter
  url: security/monte-carlo-data-trust-center.yml
- type: Compliance
  url: security/monte-carlo-data-trust-center.yml
- type: DomainSecurity
  url: security/monte-carlo-data-domain-security.yml
- type: Conformance
  url: conformance/monte-carlo-data-conformance.yml
- type: Authentication
  url: authentication/monte-carlo-data-authentication.yml
- type: OAuthScopes
  url: scopes/monte-carlo-data-scopes.yml
- type: Conventions
  url: conventions/monte-carlo-data-conventions.yml
- type: ErrorCatalog
  url: errors/monte-carlo-data-problem-types.yml
- type: Lifecycle
  url: lifecycle/monte-carlo-data-lifecycle.yml
- type: Plans
  url: plans/monte-carlo-data-plans-pricing.yml
- type: RateLimits
  url: rate-limits/monte-carlo-data-rate-limits.yml
- type: Webhooks
  url: asyncapi/monte-carlo-data-webhooks.yml
- type: AgentSkill
  url: skills/_index.yml
integrations:
- name: Snowflake
- name: Databricks
- name: Google BigQuery
- name: Amazon Redshift
- name: Microsoft Azure
- name: dbt
- name: Apache Airflow
- name: Tableau
- name: Looker
- name: Slack
- name: Microsoft Teams
- name: PagerDuty
- name: Opsgenie
- name: ServiceNow
- name: FireHydrant
- name: GitHub
- name: GitLab
- name: Azure DevOps
- name: Snowflake Cortex Agents
- name: OpenTelemetry
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
x-enrichment:
  date: '2026-08-29'
  status: enriched
  artifacts_added: 39
  pass: local-v1

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Monte Carlo

Kin Score

APIs 3

Monte Carlo GraphQL API

Monte Carlo Push Ingest API

Monte Carlo MCP Server

MCP Servers 1

Monte Carlo MCP Server

Pricing Plans 1

Monte Carlo Data Plans Pricing