{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"monte-carlo","slug":"monte-carlo","name":"Monte Carlo","type":"product","url":"https://www.montecarlodata.com","page_url":"https://unfragile.ai/monte-carlo","categories":["data-analysis"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"monte-carlo__cap_0","uri":"capability://data.processing.analysis.ml.powered.anomaly.detection.across.heterogeneous.data.sources","name":"ml-powered anomaly detection across heterogeneous data sources","description":"Automatically detects statistical anomalies in data distributions, freshness, completeness, and schema changes by applying machine learning models trained on historical data patterns. The system ingests metadata and sample data from connected warehouses/lakes, establishes baseline distributions, and flags deviations exceeding learned thresholds without requiring manual rule configuration. Supports multi-dimensional anomaly detection (row counts, column distributions, null rates, schema drift) across 20+ data platforms simultaneously.","intents":["Detect when a data pipeline produces unexpected row counts or missing values without writing custom SQL checks","Identify schema changes (new columns, type changes, deletions) automatically across all monitored tables","Catch data quality regressions before downstream consumers are impacted","Monitor distribution shifts in ML model training data that could indicate data drift"],"best_for":["Data engineering teams managing 100+ tables across multiple warehouses","ML teams needing to detect training data drift without manual monitoring","Enterprise data organizations requiring automated incident detection at scale"],"limitations":["ML models require historical baseline period (typically 2-4 weeks) before anomalies can be detected reliably","Anomaly sensitivity is not user-configurable per monitor in Start tier; requires Scale tier+ for custom thresholds","Detection latency not specified in documentation; appears to be batch-based rather than real-time streaming","False positive rates not disclosed; tuning requires escalation to support in lower tiers"],"requires":["Connection to supported data warehouse (Snowflake, Databricks, BigQuery, Redshift, etc.)","Read-only credentials with metadata query permissions","Scale tier or higher for advanced anomaly configuration (Start tier limited to 1,000 pre-configured monitors)","Minimum 2-4 weeks of historical data for baseline establishment"],"input_types":["database metadata (table schemas, column types)","sample row data for distribution analysis","query execution logs (optional, for freshness detection)"],"output_types":["anomaly incident alerts (JSON via webhook or UI)","severity classification (critical/warning/info)","affected table/column metadata","historical comparison charts"],"categories":["data-processing-analysis","anomaly-detection","ml-observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"monte-carlo__cap_1","uri":"capability://planning.reasoning.automated.root.cause.analysis.with.lineage.based.impact.assessment","name":"automated root cause analysis with lineage-based impact assessment","description":"When a data anomaly is detected, the platform automatically traces upstream data lineage to identify the source table or transformation that introduced the issue, then traces downstream to quantify impact on dependent tables, dashboards, and ML models. Uses a proprietary lineage graph built from warehouse metadata, query logs, and integration metadata to construct dependency chains. Provides incident context including affected downstream consumers and estimated business impact.","intents":["Quickly identify which upstream ETL job or source table caused a data quality incident","Understand which dashboards and ML models are affected by a data quality issue","Automate incident triage by correlating multiple anomalies to a single root cause","Reduce mean time to resolution (MTTR) by eliminating manual lineage investigation"],"best_for":["Data teams with complex multi-hop ETL pipelines (3+ transformation layers)","Organizations with 50+ downstream consumers per data asset","Incident response teams needing rapid impact assessment during outages"],"limitations":["Lineage accuracy depends on metadata completeness; custom transformations or undocumented dependencies may be missed","Root cause analysis is automated but not always correct; requires human validation for complex multi-source incidents","Lineage graph is proprietary and not portable; switching platforms requires re-establishing lineage","Does not trace lineage through non-SQL transformations (Python, Spark custom code) unless explicitly logged","Impact assessment is based on table-level dependencies; does not drill into row-level or column-level impact"],"requires":["Connection to data warehouse with query log access (Snowflake, Databricks, BigQuery, Redshift)","Integrations configured for all upstream data sources and downstream BI/ML tools","Scale tier or higher (Start tier does not include lineage or impact assessment)","Historical query logs (typically 30+ days) to establish dependency patterns"],"input_types":["warehouse query logs and execution history","table/column metadata and schema definitions","integration metadata from connected BI tools, ML platforms, and ETL systems","anomaly incident data (table name, metric, timestamp)"],"output_types":["root cause identification (upstream table/transformation)","lineage visualization (DAG showing dependencies)","impact report (list of affected downstream assets)","incident timeline with correlation analysis","recommended remediation actions (text-based suggestions)"],"categories":["planning-reasoning","data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"monte-carlo__cap_10","uri":"capability://automation.workflow.incident.triage.and.acknowledgment.workflow","name":"incident triage and acknowledgment workflow","description":"Provides incident management workflow including incident acknowledgment, assignment to team members, and status tracking (new, acknowledged, resolved, false positive). Enables teams to collaborate on incident investigation and resolution. Tracks incident state changes and provides incident history for post-mortems. Integrates with external incident management systems via webhooks for automated incident creation and routing.","intents":["Acknowledge incidents to prevent duplicate alerts and show team is investigating","Assign incidents to specific team members for investigation and resolution","Track incident status from detection through resolution","Maintain incident history for post-mortems and root cause analysis"],"best_for":["Data teams with formal incident response processes","Organizations requiring incident tracking and accountability","Teams needing to correlate incidents with external incident management systems"],"limitations":["Incident workflow appears basic; no escalation rules or SLA tracking mentioned","Assignment is manual; no automatic assignment based on ownership or expertise","Incident history retention period not specified","No integration with external incident management systems mentioned (requires webhooks for integration)","No incident severity customization; appears to use pre-defined severity levels"],"requires":["Any tier (Start tier and above)","User access to incident management UI","Optional: webhook integration with external incident management systems"],"input_types":["incident detection events (anomalies, freshness violations, etc.)","user actions (acknowledge, assign, resolve)","incident status updates"],"output_types":["incident status and state transitions","incident assignment and ownership","incident history and timeline","incident metrics (MTTR, resolution rate)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"monte-carlo__cap_11","uri":"capability://tool.use.integration.api.based.monitor.creation.and.configuration","name":"api-based monitor creation and configuration","description":"Exposes REST API for programmatic monitor creation, configuration, and management. Enables infrastructure-as-code approach to monitoring by defining monitors in code rather than UI. Supports API calls for creating anomaly detection monitors, freshness monitors, and schema change monitors. Tiered API rate limits (10K-100K calls/day depending on subscription tier). API documentation not publicly available; requires support access.","intents":["Define monitoring rules in code and version control them alongside data pipelines","Automate monitor creation for new tables as they are added to warehouse","Integrate monitoring configuration with CI/CD pipelines","Programmatically update monitor thresholds and SLA definitions"],"best_for":["Teams practicing infrastructure-as-code and GitOps","Organizations with large numbers of tables requiring automated monitor creation","Teams integrating monitoring into CI/CD pipelines"],"limitations":["API documentation is not publicly available; requires support access for implementation details","API rate limits are tiered (10K-100K calls/day); may be insufficient for large-scale automation","API authentication method not documented; likely API key-based","No SDK provided; requires direct HTTP API calls or custom wrapper","API response format and error handling not documented"],"requires":["API key or authentication credentials (method not documented)","Any tier (Start tier and above)","API rate limit appropriate for use case (10K-100K calls/day)","Support access for API documentation (not publicly available)"],"input_types":["monitor configuration (table name, metric type, thresholds)","SLA definitions (freshness windows, update frequency)","anomaly detection parameters"],"output_types":["monitor creation confirmation (monitor ID, status)","monitor configuration response (JSON)","API error responses with status codes"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"monte-carlo__cap_12","uri":"capability://data.processing.analysis.real.time.incident.dashboard.and.visualization","name":"real-time incident dashboard and visualization","description":"Provides web-based dashboard showing real-time incident status, anomaly trends, and data quality metrics across all monitored tables. Displays incident timeline, affected assets, root cause analysis results, and downstream impact. Includes visualizations for data distribution changes, freshness trends, and schema evolution. Enables drill-down from dashboard to incident details and lineage visualization.","intents":["Get real-time visibility into data quality incidents across all monitored assets","Quickly identify which tables are experiencing anomalies and their severity","Visualize data distribution changes and trend analysis","Drill down from dashboard to incident details and root cause analysis"],"best_for":["Data teams needing real-time visibility into data quality","Executives and stakeholders requiring data quality dashboards","Incident response teams needing rapid incident assessment"],"limitations":["Dashboard refresh rate not specified; appears to be near real-time but latency not documented","Customization options for dashboard not mentioned; appears to be pre-configured views","No export of dashboard data or scheduled reports mentioned","Mobile/responsive design not mentioned; appears to be web-only","Historical data retention for trend visualization not specified"],"requires":["Any tier (Start tier and above)","Web browser with JavaScript enabled","User access to Monte Carlo UI"],"input_types":["incident data (anomalies, freshness violations, schema changes)","metrics and monitoring data","root cause analysis results","lineage and impact assessment data"],"output_types":["real-time incident dashboard","incident timeline visualization","data distribution charts","freshness trend graphs","lineage visualization (DAG)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"monte-carlo__cap_13","uri":"capability://tool.use.integration.integration.with.bi.tools.and.data.catalogs","name":"integration with bi tools and data catalogs","description":"Integrates with business intelligence platforms and data catalog systems to provide data quality context within BI tools and enable impact assessment on dashboards. Enables BI users to see data quality incidents and freshness status for tables used in dashboards. Integrates with data catalogs (Collibra, Alation, etc.) to enrich metadata with data quality and freshness information. Provides bidirectional integration where BI tool ownership information is used for incident routing and escalation.","intents":["Show data quality incidents and freshness status within BI dashboards","Alert BI users when tables used in their dashboards have quality issues","Enrich data catalog metadata with data quality and freshness information","Route incidents to dashboard owners based on BI tool integration"],"best_for":["Organizations using BI tools (Tableau, Looker, Power BI, etc.) with data quality concerns","Teams using data catalogs for metadata management","Enterprises needing to surface data quality issues to business users"],"limitations":["Specific BI tool integrations not documented; appears to be limited to major platforms","Data catalog integration details not provided; requires support for implementation","Bidirectional integration (BI ownership to incident routing) not confirmed; may be one-way only","BI tool integration may require custom development or API access","No real-time sync of BI dashboard changes to Monte Carlo; may have latency"],"requires":["Connection to supported BI tool (Tableau, Looker, Power BI, etc.)","Optional: connection to data catalog (Collibra, Alation, etc.)","Scale tier or higher for advanced integrations","BI tool API access and credentials"],"input_types":["BI dashboard metadata (tables used, ownership)","data catalog metadata (asset descriptions, ownership)","incident and data quality data from Monte Carlo"],"output_types":["data quality badges/indicators in BI tools","incident notifications to BI users","enriched metadata in data catalogs","incident routing based on BI ownership"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"monte-carlo__cap_2","uri":"capability://planning.reasoning.agent.and.llm.output.observability.with.context.and.behavior.tracking","name":"agent and llm output observability with context and behavior tracking","description":"Monitors AI agent execution including context window contents, function calls, tool invocations, and output quality. Tracks agent behavior patterns (decision paths, tool selection frequency, error rates) and detects anomalies in agent outputs (hallucinations, inconsistent responses, unexpected tool usage). Integrates with LangChain and Databricks Genie to capture agent telemetry without code instrumentation. Provides incident alerts when agent behavior deviates from baseline patterns or output quality degrades.","intents":["Detect when an AI agent starts producing hallucinated or incorrect outputs","Monitor which tools/functions an agent is using and identify unexpected tool selection patterns","Track agent context window usage and identify when context is insufficient for accurate responses","Alert on agent failures or error rates exceeding baseline thresholds"],"best_for":["Teams deploying LangChain agents in production requiring observability","Organizations running Databricks Genie agents needing behavior monitoring","ML teams monitoring LLM application quality and detecting output degradation"],"limitations":["Integration is limited to LangChain and Databricks Genie; other agent frameworks require custom instrumentation","Agent output quality detection is based on statistical anomalies, not semantic understanding; cannot detect subtle hallucinations","Context window tracking requires agent framework integration; does not work with black-box LLM APIs","Baseline behavior patterns require 2-4 weeks of historical agent execution data","No built-in feedback loop for agent retraining; observability only, not automated remediation"],"requires":["LangChain agent or Databricks Genie deployment","Scale tier or higher (agent observability not available in Start tier)","Agent framework integration enabled (automatic for LangChain/Genie, manual for others)","Historical agent execution logs (2-4 weeks minimum for baseline establishment)"],"input_types":["agent execution traces (function calls, tool invocations, context)","LLM prompt and completion logs","agent output data (responses, decisions, tool selections)","error logs and exception traces"],"output_types":["agent behavior anomaly alerts (JSON via webhook or UI)","tool usage frequency reports","context window utilization metrics","output quality degradation alerts","agent execution timeline with decision paths"],"categories":["planning-reasoning","tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"monte-carlo__cap_3","uri":"capability://data.processing.analysis.multi.warehouse.schema.and.metadata.synchronization","name":"multi-warehouse schema and metadata synchronization","description":"Continuously ingests and synchronizes table schemas, column definitions, and metadata from connected data warehouses and lakes. Detects schema changes (new columns, type changes, deletions, renames) and tracks schema evolution history. Maintains a unified metadata view across Snowflake, Databricks, BigQuery, Redshift, and other platforms. Provides schema change notifications and impact analysis when schemas are modified.","intents":["Get notified immediately when a table schema changes in production","Track which columns were added/removed/modified and when","Understand impact of schema changes on downstream dashboards and ML models","Maintain audit trail of all schema modifications for compliance"],"best_for":["Data teams managing schemas across multiple warehouses","Organizations requiring schema change governance and audit trails","Teams needing to detect breaking schema changes before they impact consumers"],"limitations":["Schema change detection is based on metadata snapshots; does not capture intermediate changes if snapshots are infrequent","Does not track column-level lineage or data type transformations through ETL","Schema change impact analysis is table-level only; does not identify affected specific columns in downstream queries","Rename detection requires heuristic matching; may miss renames if column names change significantly","No built-in schema versioning or rollback capabilities; observability only"],"requires":["Connection to supported data warehouse (Snowflake, Databricks, BigQuery, Redshift, etc.)","Read-only credentials with schema/metadata query permissions","Any tier (Start tier and above)","Metadata query permissions on all monitored schemas"],"input_types":["warehouse metadata APIs (INFORMATION_SCHEMA, DESCRIBE TABLE, etc.)","schema definition snapshots","table and column metadata"],"output_types":["schema change alerts (JSON via webhook or UI)","schema change history/audit log","column-level change details (name, type, nullable, default)","schema comparison reports","impact analysis on downstream assets"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"monte-carlo__cap_4","uri":"capability://data.processing.analysis.freshness.and.sla.monitoring.with.automated.alerting","name":"freshness and sla monitoring with automated alerting","description":"Monitors data freshness by tracking table update frequency, last-modified timestamps, and query execution patterns. Establishes freshness baselines (e.g., 'table should be updated daily by 9 AM') and alerts when tables fall outside SLA windows. Integrates with query logs to detect when expected ETL jobs fail to complete. Provides incident context including last successful update time, current lag, and estimated time to SLA breach.","intents":["Get alerted when a critical table hasn't been updated within its expected SLA window","Detect when an ETL job fails silently without updating downstream tables","Track data freshness across all tables and identify chronic SLA violations","Correlate freshness issues with upstream job failures or data quality problems"],"best_for":["Data teams managing SLAs for critical data assets","Organizations with time-sensitive analytics or reporting requirements","Teams needing to detect ETL job failures without manual monitoring"],"limitations":["Freshness detection relies on table modification timestamps; does not work for append-only tables without explicit update tracking","SLA windows must be manually configured per table; no automatic SLA inference","Does not distinguish between intentional data delays and actual failures","Freshness alerts are based on table-level timestamps; does not track row-level freshness or incremental updates","Start tier limited to 1,000 monitors; large deployments require Scale tier+"],"requires":["Connection to supported data warehouse with query log access","Read-only credentials with table modification timestamp access","Manual SLA configuration per table or table group","Any tier (Start tier and above)"],"input_types":["table modification timestamps (LAST_MODIFIED, etc.)","query execution logs (for ETL job tracking)","manual SLA definitions (update frequency, time windows)"],"output_types":["freshness violation alerts (JSON via webhook or UI)","SLA breach notifications with time-to-breach estimates","freshness metrics dashboard (last update time, lag, SLA status)","historical freshness trends","correlated incident reports (freshness + upstream failures)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"monte-carlo__cap_5","uri":"capability://tool.use.integration.webhook.based.incident.notification.and.integration","name":"webhook-based incident notification and integration","description":"Sends real-time incident notifications to external systems via webhooks when anomalies are detected. Supports integration with incident management platforms (ServiceNow, PagerDuty implied), Slack, and custom HTTP endpoints. Webhooks include full incident context (affected table, anomaly type, root cause, impact assessment, severity). Enables automated incident creation, escalation, and routing based on incident severity and affected asset ownership.","intents":["Route data quality incidents to Slack channels for immediate visibility","Automatically create ServiceNow incidents when critical data anomalies are detected","Trigger PagerDuty alerts for high-severity data incidents requiring immediate response","Integrate incident notifications with custom incident management workflows"],"best_for":["Teams using incident management platforms (ServiceNow, PagerDuty, Opsgenie)","Organizations with Slack-based incident response workflows","Teams needing to automate incident creation and routing based on data quality"],"limitations":["Webhook delivery is not guaranteed; no built-in retry logic or delivery confirmation mentioned","Webhook payload schema is not documented; integration requires reverse-engineering from examples","No webhook filtering or routing rules; all incidents sent to all configured webhooks","Scale tier and above only; Start tier does not support webhooks","Webhook delivery latency not specified; appears to be near real-time but not guaranteed"],"requires":["Scale tier or higher (webhooks not available in Start tier)","HTTPS endpoint for webhook delivery","Integration with target platform (ServiceNow, Slack, PagerDuty, etc.)","Webhook URL configuration in Monte Carlo UI"],"input_types":["anomaly incident data (table, metric, severity, timestamp)","root cause analysis results","impact assessment data","lineage and downstream asset information"],"output_types":["JSON webhook payload with incident context","incident creation in external systems (ServiceNow, PagerDuty)","Slack message notifications","custom HTTP POST to user-defined endpoints"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"monte-carlo__cap_6","uri":"capability://data.processing.analysis.data.export.and.self.hosted.storage.option","name":"data export and self-hosted storage option","description":"Provides capability to export incident data, metrics, and audit logs from Monte Carlo platform for external analysis or compliance archival. Supports self-hosted storage option (Scale tier+) where monitoring data can be stored in customer-controlled infrastructure instead of Monte Carlo SaaS. Enables data residency compliance and reduces vendor lock-in by allowing data portability.","intents":["Export incident history and metrics for external analysis or reporting","Store monitoring data in customer-controlled infrastructure for data residency compliance","Archive audit logs for compliance and regulatory requirements","Reduce vendor lock-in by maintaining portable copy of monitoring data"],"best_for":["Organizations with data residency requirements (GDPR, CCPA, etc.)","Teams requiring long-term archival of incident and audit data","Enterprises evaluating Monte Carlo and concerned about vendor lock-in"],"limitations":["Self-hosted storage option requires Scale tier or higher; not available in Start tier","Export format and API not documented; requires support request for implementation details","Self-hosted storage still requires Monte Carlo SaaS for monitoring and analysis; not a full on-premises solution","Data export frequency and latency not specified","No built-in data transformation or normalization for exported data"],"requires":["Scale tier or higher for self-hosted storage option","Customer-controlled storage infrastructure (S3, GCS, Azure Blob, etc.)","Network connectivity from Monte Carlo platform to customer storage","Any tier for basic data export (Start tier and above)"],"input_types":["incident data (anomalies, root causes, impact)","metrics and monitoring data","audit logs and change history","lineage and metadata"],"output_types":["JSON or CSV export files","data streamed to customer-controlled storage","audit log archives","compliance reports"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"monte-carlo__cap_7","uri":"capability://safety.moderation.pii.detection.and.filtering.in.monitored.data","name":"pii detection and filtering in monitored data","description":"Automatically detects and filters personally identifiable information (PII) in monitored data samples and incident reports. Identifies common PII patterns (email addresses, phone numbers, SSNs, credit card numbers, etc.) and redacts or masks them before displaying in UI or sending in notifications. Prevents accidental exposure of sensitive data in incident alerts and audit logs.","intents":["Prevent PII from being exposed in incident notifications and alerts","Ensure compliance with data privacy regulations (GDPR, CCPA) in monitoring data","Redact sensitive information from audit logs and incident reports","Maintain data privacy while monitoring sensitive tables"],"best_for":["Organizations handling personally identifiable information (healthcare, finance, retail)","Teams subject to data privacy regulations (GDPR, CCPA, HIPAA)","Enterprises requiring PII protection in all monitoring and alerting systems"],"limitations":["PII detection is pattern-based; may miss custom or obfuscated PII formats","Filtering is applied to displayed data only; does not prevent PII from being stored in backend","Scale tier and above only; Start tier does not include PII filtering","No custom PII pattern definition; limited to pre-configured patterns","Redaction strategy (masking, hashing, removal) not configurable per data type"],"requires":["Scale tier or higher (PII filtering not available in Start tier)","Manual enablement of PII filtering in platform settings","No additional configuration required; uses pre-configured PII patterns"],"input_types":["data samples from monitored tables","incident alert content","audit logs and change history"],"output_types":["redacted/masked incident alerts","filtered audit logs","PII detection reports (count of PII fields detected)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"monte-carlo__cap_8","uri":"capability://safety.moderation.audit.logging.and.compliance.reporting","name":"audit logging and compliance reporting","description":"Maintains comprehensive audit logs of all platform actions including monitor creation/modification, incident acknowledgment, user access, and configuration changes. Provides audit trail for compliance and regulatory requirements. Generates compliance reports showing who accessed what data, when, and what actions were taken. Supports SCIM and SSO for identity management integration.","intents":["Maintain audit trail of all monitoring configuration changes for compliance","Track who accessed incident data and when for security and compliance","Generate compliance reports for regulatory audits (SOC2, HIPAA, etc.)","Integrate with enterprise identity management (SSO, SCIM)"],"best_for":["Enterprises subject to compliance requirements (SOC2, HIPAA, PCI-DSS)","Organizations with strict access control and audit requirements","Teams using enterprise identity management (Okta, Azure AD, etc.)"],"limitations":["Audit logs are Scale tier and above only; Start tier does not include audit logging","Audit log retention period not specified; may have limits on historical data","Compliance report generation appears manual; no automated report scheduling mentioned","SCIM/SSO integration details not documented; requires support for implementation","No fine-grained access control; audit logging is all-or-nothing per user"],"requires":["Scale tier or higher (audit logging not available in Start tier)","Enterprise identity provider (Okta, Azure AD, etc.) for SSO/SCIM integration","Manual compliance report request to support team"],"input_types":["user actions (login, configuration changes, incident acknowledgment)","data access events (who viewed which incidents/tables)","system events (monitor creation, integration changes)"],"output_types":["audit log entries (JSON format, queryable in UI)","compliance reports (PDF or CSV)","access control reports","change history and audit trails"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"monte-carlo__cap_9","uri":"capability://safety.moderation.multi.tier.user.access.control.and.role.based.permissions","name":"multi-tier user access control and role-based permissions","description":"Implements role-based access control (RBAC) with configurable user permissions for viewing incidents, modifying monitors, and accessing sensitive data. Supports user tiers (Start tier limited to 10 users, Scale tier unlimited) and role definitions (implied: admin, analyst, viewer based on typical RBAC patterns). Enables granular control over who can create monitors, acknowledge incidents, and export data.","intents":["Restrict incident visibility to specific teams or departments","Control who can create or modify monitoring rules","Limit data export and sensitive information access to authorized users","Manage user access across large teams with different responsibilities"],"best_for":["Large organizations with multiple teams needing access to different data assets","Enterprises requiring fine-grained access control for compliance","Teams with different roles (data engineers, analysts, executives) needing different visibility"],"limitations":["Start tier limited to 10 users; Scale tier required for larger teams","Specific RBAC roles and permissions not documented; requires support for details","No asset-level access control mentioned; appears to be user-level only","Role definitions appear to be pre-configured; no custom role creation mentioned","Access control integration with external identity providers (LDAP, AD) not mentioned"],"requires":["Start tier: maximum 10 users","Scale tier or higher: unlimited users","User management in Monte Carlo UI or via SCIM integration"],"input_types":["user identity and role assignment","permission definitions (view, create, modify, export)","resource access requests"],"output_types":["access control decisions (allow/deny)","audit logs of access attempts","user permission reports"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"monte-carlo__headline","uri":"capability://data.processing.analysis.enterprise.data.observability.platform","name":"enterprise data observability platform","description":"Monte Carlo is an enterprise data observability platform that leverages machine learning to detect anomalies, schema changes, and data freshness issues across your data stack, providing automated insights and root cause analysis for data incidents.","intents":["best data observability platform","data observability for enterprise analytics","top tools for monitoring data quality","data anomaly detection solutions","how to ensure data freshness in pipelines"],"best_for":["data engineers","data scientists","analytics teams"],"limitations":["may not provide deep insights into ML algorithms"],"requires":["access to data sources"],"input_types":["data from warehouses, databases, BI tools"],"output_types":["alerts, dashboards, reports"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":54,"verified":false,"data_access_risk":"high","permissions":["Connection to supported data warehouse (Snowflake, Databricks, BigQuery, Redshift, etc.)","Read-only credentials with metadata query permissions","Scale tier or higher for advanced anomaly configuration (Start tier limited to 1,000 pre-configured monitors)","Minimum 2-4 weeks of historical data for baseline establishment","Connection to data warehouse with query log access (Snowflake, Databricks, BigQuery, Redshift)","Integrations configured for all upstream data sources and downstream BI/ML tools","Scale tier or higher (Start tier does not include lineage or impact assessment)","Historical query logs (typically 30+ days) to establish dependency patterns","Any tier (Start tier and above)","User access to incident management UI"],"failure_modes":["ML models require historical baseline period (typically 2-4 weeks) before anomalies can be detected reliably","Anomaly sensitivity is not user-configurable per monitor in Start tier; requires Scale tier+ for custom thresholds","Detection latency not specified in documentation; appears to be batch-based rather than real-time streaming","False positive rates not disclosed; tuning requires escalation to support in lower tiers","Lineage accuracy depends on metadata completeness; custom transformations or undocumented dependencies may be missed","Root cause analysis is automated but not always correct; requires human validation for complex multi-source incidents","Lineage graph is proprietary and not portable; switching platforms requires re-establishing lineage","Does not trace lineage through non-SQL transformations (Python, Spark custom code) unless explicitly logged","Impact assessment is based on table-level dependencies; does not drill into row-level or column-level impact","Incident workflow appears basic; no escalation rules or SLA tracking mentioned","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.328Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=monte-carlo","compare_url":"https://unfragile.ai/compare?artifact=monte-carlo"}},"signature":"ILDbUhZ0NgZ7C+3TIQJomfbv3gyyFSeM2FF59ePfvKFbwZfaubDkFRz8I40fq11BUs1RCn4bXQdJ5gsEJXf7BA==","signedAt":"2026-06-20T17:38:20.962Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/monte-carlo","artifact":"https://unfragile.ai/monte-carlo","verify":"https://unfragile.ai/api/v1/verify?slug=monte-carlo","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}