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: Graph QL 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, API reference, CLI, and 30 more developer resources.

65.5/100 strong ▬ flat Agent 59/100 agent ready Front door AI 6/6 dominant saas Full breakdown ↓
scored 2026-09-21 · rubric v0.22.0
1 APIs 1 MCP Servers
Data ObservabilityData QualityData ReliabilityData LakeData WarehouseLineageMonitoringAI Observability

Kin Score

Kin Score Kin Score How this is scored →
scored 2026-09-21 · rubric v0.22.0
Create-or-Update Ergonomics applies to this provider. This API accepts writes, so it carries 10 points of the composite. It is scored from the published contracts themselves: whether a caller can create-or-update in one call, whether the write accepts a key the caller already holds, and whether the response says which branch ran. Without that, every write needs a search-and-branch in front of it, and the first time that check is skipped a duplicate record is created. Scored against the observed mean rather than raw — a provider at the catalog average is unchanged by this facet, not penalised by it.
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 github.com/api-evangelist/monte-carlo: open an issue to ask a question, or submit a pull request to add artifacts. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

APIs 4

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 Push Ingest API

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 ...

Monte Carlo MCP Server

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 tu...

Monte Carlo Graph QL API

The Graph QL API from Monte Carlo — 1 operation(s) for graph ql.

Open Collections 3

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

API Collection

OPEN COLLECTION

Monte Carlo Graphql API

OPEN COLLECTION

Monte Carlo GraphQL API

OPEN COLLECTION

MCP Servers 1

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

Monte Carlo MCP Server

First-party, fully hosted Model Context Protocol server for the Monte Carlo data + AI observability platform. Lets an agent investigate alerts, explore assets and lineage, creat...

MCP SERVER

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

Pricing Plans 1

Published pricing tiers and plan structures.

Rate Limits 1

Documented rate limits and quota policies.

Monte Carlo Rate Limits

5 limits

RATE LIMITS

Security Posture 4

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

Monte Carlo Authentication

apiKey/oauth2 · 5 schemes

SECURITY

Monte Carlo Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Monte Carlo Vulnerability Disclosure

security.txt · contact published

SECURITY

Monte Carlo Trust Center

SOC 2, ISO 27001

SECURITY

Scopes 1

OAuth scopes governing access to this provider's APIs.

Monte Carlo Scopes

6 scopes

6 scopes

SCOPES

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 4

Portal, sign-up, and the first successful call

Documentation 2

Reference material describing how the API behaves

Agent Surfaces 4

MCP servers, agent skills, and machine-readable catalogs

Design & Contract 4

Pagination, idempotency, versioning, errors, and events

Build 4

SDKs, sample code, and the tooling you integrate with

Access & Security 6

Authentication, authorization, and security posture

Operate 5

Status, limits, changes, and where to get help

Commercial 4

Pricing, plans, and the legal terms of use

Company 3

The organization behind the API

Other 1

Properties that don't map to a standard resource type

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
deliveryModel:
  model: saas
  open_source: false
  commercial: true
  callable_host: true
  label: Hosted service · you call their endpoint
  confidence: high
  source:
  - openapi
  - pricing
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source:
  - authentication
  - security
  generated: '2026-09-03'
  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: '2026-09-16'
specificationVersion: '0.23'
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: DeveloperPortal
    url: https://docs.getmontecarlo.com/docs/developer-resources
  - type: Documentation
    url: https://docs.getmontecarlo.com/docs/data-lakes
  - type: Documentation
    url: https://docs.getmontecarlo.com/docs/mcp-server
  - url: graphql/monte-carlo-graphql.md
    type: GraphQL
  - type: APIReference
    url: https://apidocs.getmontecarlo.com/
  - type: Authentication
    url: authentication/monte-carlo-authentication.yml
  - type: OAuthScopes
    url: scopes/monte-carlo-scopes.yml
  - type: ErrorCatalog
    url: errors/monte-carlo-problem-types.yml
  - type: RateLimits
    url: rate-limits/monte-carlo-rate-limits.yml
- aid: monte-carlo: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-problem-types.yml
- aid: monte-carlo: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.
    25 tools listed in the Claude Connectors Directory 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-mcp.yml
  - type: ToolCrosswalk
    url: mcp/monte-carlo-tool-crosswalk.yml
  - type: Documentation
    url: https://docs.getmontecarlo.com/docs/mcp-server
  - type: AgentSkill
    url: skills/_index.yml
  - type: Documentation
    url: https://docs.getmontecarlo.com/docs/mcp-server-reference-and-operations
- aid: monte-carlo:monte-carlo-graph-ql-api
  name: Monte Carlo Graph QL API
  description: The Graph QL API from Monte Carlo — 1 operation(s) for graph ql.
  humanURL: https://docs.getmontecarlo.com/docs/api
  baseURL: https://api.getmontecarlo.com/graphql
  tags:
  - GraphQL
  tags_raw:
  - Graph QL
  properties:
  - type: OpenAPI
    url: openapi/monte-carlo-graph-ql-api-openapi.yml
  - type: Overlay
    url: overlays/monte-carlo-graph-ql-api-overlay.yaml
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-ai
- type: Website
  url: https://montecarlo.ai/
- type: Documentation
  url: https://docs.getmontecarlo.com
- type: GitHubOrganization
  url: https://github.com/monte-carlo-data
- type: Pricing
  url: https://montecarlo.ai/request-for-pricing/
- type: Signup
  url: https://montecarlo.ai/request-a-demo
- type: LlmsText
  url: https://docs.getmontecarlo.com/llms.txt
- url: https://montecarlo.ai/blog/feed
  type: Blog
- type: APIReference
  url: https://apidocs.getmontecarlo.com/
- type: WellKnown
  url: well-known/monte-carlo-well-known.yml
- type: SecurityTxt
  url: well-known/monte-carlo-security.txt
- type: APICatalog
  url: well-known/monte-carlo-api-catalog.json
- type: Security
  url: security/monte-carlo-vulnerability-disclosure.yml
- type: Compliance
  url: security/monte-carlo-trust-center.yml
- type: Packages
  url: packages/monte-carlo-packages.yml
- type: SDKs
  url: packages/monte-carlo-packages.yml
- type: CLI
  url: cli/monte-carlo-cli.yml
- type: ChangeLog
  url: changelog/monte-carlo-changelog.yml
- type: ChangeLog
  url: https://docs.getmontecarlo.com/changelog
- type: Plans
  url: plans/monte-carlo-plans-pricing.yml
- type: RateLimits
  url: rate-limits/monte-carlo-rate-limits.yml
- type: Lifecycle
  url: lifecycle/monte-carlo-lifecycle.yml
- type: StatusPage
  url: https://status.getmontecarlo.com/
- type: Deprecation
  url: lifecycle/monte-carlo-lifecycle.yml
- type: Conventions
  url: conventions/monte-carlo-conventions.yml
- type: Conformance
  url: conformance/monte-carlo-conformance.yml
- type: Webhooks
  url: webhooks/monte-carlo-webhooks.yml
- type: LLMsTxt
  url: llms/monte-carlo-llms.txt
- type: GettingStarted
  url: https://docs.getmontecarlo.com/docs/welcome
- type: TermsOfService
  url: https://montecarlo.ai/terms-of-service
- type: PrivacyPolicy
  url: https://montecarlo.ai/privacy-policy
- type: Login
  url: https://getmontecarlo.com/signin/
- type: DeveloperPortal
  url: https://docs.getmontecarlo.com/docs/developer-resources
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
x-enrichment:
  date: '2026-09-16'
  status: enriched
  artifacts_added: 45
  pass: local-v3

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