montecarlodata
Monte Carlo's CLI
Decision gist · record as of 2026-08-14
Yes, if you use Monte Carlo for data observability and need programmatic or scripted access to platform operations. The CLI is actively maintained (released 1 day ago), has low install friction, and integrates well with CI/CD and dbt workflows. No known security vulnerabilities. Apache 2.0 license is permissive. Not relevant if you don't use Monte Carlo or prefer web-only interaction.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or greater.
- MCD_DEFAULT_API_ID and MCD_DEFAULT_API_TOKEN environment variables or interactive configuration are required to authenticate.
- Low friction install with a pure Python wheel.
License · maintenance · safety
permissive license (permissive) — Apache 2.0 permissive license allows commercial and private use with minimal restrictions; suitable for both open-source and proprietary projects.
last release 2026-08-13 (1 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 184,557 downloads/mo, #10,031 on PyPI
Alternatives
Verify before relying
pip install montecarlodata
montecarlo configure
montecarlo --version
montecarlo validate- Whether the CLI supports all Monte Carlo platform features or a subset of the full API surface.
- Performance characteristics and typical latency for large-scale monitor or integration operations.
- Compatibility with specific data warehouse platforms beyond the dbt example shown.
What it is and what it does
montecarlodata is the official CLI for Monte Carlo, a data observability platform. It provides a command-line interface to configure authentication, manage integrations, deploy data quality monitors, and interact with the data catalog—all without leaving the terminal. The tool is designed for both interactive use (with guided prompts) and scripted/CI workflows (supporting non-interactive API key or OAuth authentication).
The package bundles 13 runtime dependencies including click for CLI scaffolding, boto3 for AWS integration, Jinja2 for templating, and questionary for interactive prompts. It supports Python 3.10 and later and is actively maintained. Primary use cases include setting up Monte Carlo profiles, listing and managing integrations, applying monitor configurations from YAML files, and importing dbt manifests into the Monte Carlo catalog.
Use it for
- Configure and validate Monte Carlo authentication in CI/CD pipelines without manual prompts using API key or OAuth credentials.
- Deploy data quality monitors in bulk by applying YAML-based monitor configurations to multiple tables and datasets.
- Import dbt project metadata into Monte Carlo's catalog to enable lineage tracking and automated quality monitoring.
- List and manage active integrations (data sources, warehouses) from the command line for auditing or automation.
- Set up multiple named profiles with custom config paths for managing different Monte Carlo environments or teams.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you use Monte Carlo for data observability and need programmatic or scripted access to platform operations.
The CLI is actively maintained (released 1 day ago), has low install friction, and integrates well with CI/CD and dbt workflows. No known security vulnerabilities. Apache 2.0 license is permissive. Not relevant if you don't use Monte Carlo or prefer web-only interaction.
Install
montecarlodata on PyPI
Before you install
Low friction install with a pure Python wheel. Actively maintained with a release 1 day old. Requires Python 3.10 or later; 13 runtime dependencies are all well-established packages (boto3, click, Jinja2, requests, etc.), suggesting a stable dependency graph.
Requires Python 3.10 or greater. MCD_DEFAULT_API_ID and MCD_DEFAULT_API_TOKEN environment variables or interactive configuration are required to authenticate.
License in practice
Apache 2.0 permissive license allows commercial and private use with minimal restrictions; suitable for both open-source and proprietary projects.
Quickstart
pip install montecarlodata
montecarlo configure
montecarlo --version
montecarlo validateVerify before relying
- Whether the CLI supports all Monte Carlo platform features or a subset of the full API surface.
- Performance characteristics and typical latency for large-scale monitor or integration operations.
- Compatibility with specific data warehouse platforms beyond the dbt example shown.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 13 packagesboto3click-config-fileclickdataclasses-jsonJinja2pycarlopython-boxPyYAMLquestionaryrequestsretrytabulatesetuptools |
| Maintenance | Actively maintained 1 days since the last release |
| First released | |
| Downloads | 184,557 / month, #10,031 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseNatural Language :: EnglishProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Topic :: Software Development :: Build Tools |
Evidence: montecarlodata-0.175.0-py3-none-any.whl
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See also pycarlo · airflow-mcd · elementary-data · tensorflow-probability · teradataml · dbt · pymc3 · dbt-mcp · datacontract-cli · emcee