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 4 API contracts indexed on the APIs.io network, including Graph QL API, and 3 more. Tagged areas include Data Observability, Data Quality, Data Reliability, Data Lake, and Data Warehouse.

The Monte Carlo catalog on APIs.io includes 1 Spectral governance ruleset.

Monte Carlo’s developer surface includes authentication, documentation, pricing, signup flow, engineering blog, API reference, CLI, and 32 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

71.4/100 exemplar ▬ flat Agent 59/100 agent ready Front door AI 6/6 dominant Heartbeat 22/100 dormant saas Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

AccessPaid

1 published contract 1 MCP Servers 19 Agent Skills

Data Observability Data Quality Data Reliability Data Lake Data Warehouse Data Lineage Monitoring AI Observability Data Monitoring GraphQL

What people ask Monte Carlo 1

The questions people put to an LLM, and the jobs they hand an agent, that Monte Carlo's APIs answer — each one linked to the operation that does it. Generated by API Evangelist

Monte Carlo Graph QL API Run a GraphQL query or mutation against Monte Carlo “How do I pull my data incidents and monitors out of Monte Carlo programmatically?”

Kin Score

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

Composite quality — 71.4/100 · exemplar

Contract Quality 10.0 / 19

Developer Ergonomics 11.1 / 15

Access Clarity 12.8 / 15

Operational Transparency 9.0 / 10

Contract Governance 2.9 / 9

Discoverability 5.6 / 8

Regulatory Posture 5.9 / 15

Create-or-Update Ergonomics 0.0 / 10

Agent readiness — 59/100 · agent ready

Machine-Readable Contract 18 / 18

Agentic Access Contract 10 / 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 7 / 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 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 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.

The six quality facets above are damped to 75 points between them, because the conditional facet above carries the other 25. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 75% 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):

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
Registration Without a Human +6 pts — a `registration_endpoint` in the served RFC 8414 / OIDC discovery document
Documented Reversibility +6 pts — a reversal operation in the contract, graded up when a window is documented too
Agentic Commerce Well-Known Document +5 pts — a parsing /.well-known/ucp.json or acp.json declaring a version and at least one service, OR (0.27) a live x402 payment challenge from _data/x402-adoption.json — `self` when its endpoints are on the provider's own domain, `platform` when they are on a vendor's
Dry-Run / Simulate Mode +4 pts — OpenAPI: destructive operations expose a dry_run / simulate / preview / validate_only parameter

1 more unmet check in the Agent Readiness table above.

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

Contract Quality scores 53.2 — up to +11.7 composite points
Contract Governance scores 31.8 — up to +8.2 composite points
Developer Ergonomics scores 73.8 — up to +5.2 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/monte-carlo/apis.yml: open an issue on github.com/api-evangelist/monte-carlo 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
Data Integration (ELT connectors and data governance)	builds-on	17.2 (thin)	-	1
Agent Skills (SKILL.md packages)	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 5

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)

Agent Skills

measured — observed by APIs.io (published)

OAuth 2.0 Protected Resource Metadata

measured — observed by APIs.io (verified)

OpenAPI

measured — observed by APIs.io (published)

OAuth 2.0

measured — observed by APIs.io (declared)

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 2

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

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

Agent Skills 19

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

monte-carlo-analyze-root-cause

AGENT SKILL

monte-carlo-asset-health

AGENT SKILL

automated-triage

AGENT SKILL

connection-auth-rules

AGENT SKILL

monte-carlo-context-detection

AGENT SKILL

generate-validation-notebook

AGENT SKILL

monte-carlo-incident-response

AGENT SKILL

monte-carlo-instrument-agent

AGENT SKILL

monte-carlo-manage-mac

AGENT SKILL

monte-carlo-monitoring-advisor

AGENT SKILL

monte-carlo-performance-diagnosis

AGENT SKILL

monte-carlo-prevent

AGENT SKILL

monte-carlo-proactive-monitoring

AGENT SKILL

push-ingestion

AGENT SKILL

monte-carlo-reinforce-agent

AGENT SKILL

monte-carlo-remediation

AGENT SKILL

monte-carlo-storage-cost-analysis

AGENT SKILL

monte-carlo-troubleshoot-agent-traces

AGENT SKILL

tune-monitor

AGENT SKILL

Scroll for all 19

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

Monte Carlo API pricing

Prices published by Monte Carlo, read 2026-10-28 from montecarlo.ai/request-for-pricing.

Plan	Price
Start	Request pricing Includes: Up to 10 users; Pay per monitor, up to 1,000; 10,000 API calls / day
Scale	Request pricing Includes: Unlimited users; Pay per monitor; 50,000 API calls / day
Enterprise	Request pricing Includes: Unlimited users; Pay per monitor; 100,000 API calls / day
Business Critical	Request pricing Includes: Unlimited users; Pay per monitor; 100,000 API calls / day; Dedicated Instance

Plan details, limits and sources →

Rate limits

graphql: 10000 1d
graphql: 50000 1d
graphql: 100000 1d

All 5 limits →

Pricing Plans 1

Published pricing tiers and plan structures.

Monte Carlo Plans Pricing

4 plans

PLANS

Rate Limits 1

Documented rate limits and quota policies.

Monte Carlo Rate Limits

5 limits

RATE LIMITS

Service Levels 1

Uptime the provider promises or reports, as SLA4OAI. Read from its own pages; APIs.io runs no monitors.

Monte Carlo

SLA

FinOps 1

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

Monte Carlo Finops

FINOPS

Spectral Rules 1

Spectral governance rulesets for linting and validating these APIs.

Monte Carlo API Rules

48 rules · 46 errors 1 warnings 1 info

SPECTRAL

OpenAPI Overlays 2

Overlays applied on top of this provider's contracts. Each card says who wrote it: a document the provider publishes, or one API Evangelist derived or generated.

Monte Carlo Graph Ql Api Overlay

5 actions · update · monte-carlo-graph-ql-api-openapi.yml

GENERATED

Monte Carlo Graph Ql Api Phrasing Overlay

2 actions · phrasing · monte-carlo-graph-ql-api-openapi.yml

GENERATED

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

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

Signup

Signup

GettingStarted

GettingStarted

Login

DeveloperPortal

DeveloperPortal

Documentation 2

Reference material describing how the API behaves

Documentation

Documentation

APIReference

APIReference

Agent Surfaces 4

MCP servers, agent skills, and machine-readable catalogs

AgenticAccess

LlmsText

WellKnown

LLMsTxt

Design & Contract 4

Pagination, idempotency, versioning, errors, and events

Lifecycle

Conventions

Conformance

Webhooks

Build 4

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

Packages

SDKs

CLI

Access & Security 6

Authentication, authorization, and security posture

TrustCenter

DomainSecurity

Authentication

SecurityTxt

Security

Compliance

Operate 5

Status, limits, changes, and where to get help

ChangeLog

ChangeLog

RateLimits

StatusPage

Deprecation

Commercial 4

Pricing, plans, and the legal terms of use

Pricing

Plans

TermsOfService

PrivacyPolicy

Company 4

The organization behind the API

LinkedIn

Website

Website

Blog

Blog

Blog

Blog

Other 2

Properties that don't map to a standard resource type

Overlay

Overlay

APICatalog

APICatalog

Source (apis.yml)

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: paid
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Paid
  confidence: high
  source:
  - plans
  - authentication
  - scopes
  - rate-limits
  - security
  generated: '2026-11-06'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/monte-carlo.png
x-docs-platform: readme
x-docs-platform-confidence: high
x-docs-platform-checked: 2026-10-04
tags:
- Data Observability
- Data Quality
- Data Reliability
- Data Lake
- Data Warehouse
- Data Lineage
- Monitoring
- AI Observability
- Data Monitoring
- GraphQL
tags_raw:
- Data Observability
- Data Quality
- Data Reliability
- Data Lake
- Data Warehouse
- Lineage
- Monitoring
- AI Observability
- Data Monitoring
- GraphQL
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
  - Data Lineage
  tags_raw:
  - 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
common:
- type: Overlay
  url: overlays/monte-carlo-graph-ql-api-overlay.yaml
- 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
- type: Blog
  url: https://montecarlo.ai/blog
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
x-enrichment:
  date: '2026-09-16'
  status: enriched
  artifacts_added: 45
  pass: local-v3

Monte Carlo API — frequently asked questions

Does Monte Carlo have an API?

Yes. APIs.io lists 4 APIs from Monte Carlo, including Monte Carlo GraphQL API, Monte Carlo Push Ingest API and Monte Carlo MCP Server, and 1 more.

Is there an OpenAPI specification for the Monte Carlo API?

Yes. APIs.io indexes 1 OpenAPI specification for Monte Carlo, each linked for download from this page.

How do I authenticate with the Monte Carlo API?

Monte Carlo documents its authentication (apiKey/oauth2 · 5 schemes). The details are profiled on APIs.io as Monte Carlo Authentication.

How much does the Monte Carlo API cost?

Monte Carlo publishes its prices; its Start plan is Request pricing as of 2026-10-28. APIs.io lists 4 pricing plans for Monte Carlo, with each plan's price, limits and inclusions under Pricing on this page.

Does Monte Carlo have an MCP server?

Yes. APIs.io lists 1 MCP server for Monte Carlo: 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, create and tune monitors, an

Is the Monte Carlo API ready for AI agents?

Monte Carlo has a Kin Score of 71.4 out of 100 (exemplar) and an Agent Readiness score of 58.6 (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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apis_io_searchSTART HERE — APIs, providers and tags for one query, each with its total.
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