Monte Carlo
About
Monte Carlo provides data and AI observability for monitoring data reliability. Its connector gives assistants observability context for investigating data incidents, understanding quality signals, and supporting trusted analytics workflows.
Details
- Transport
- SSE
Explore
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Monte CarloCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
{
"mcpServers": {
"monte-carlo": {
"type": "http",
"url": "https://integrations.getmontecarlo.com/mcp"
}
}
}
search
What it does: Find assets by name/metadata matching. Results show references to search terms, not confirmed data dependencies. Use lineage tools to verify actual data flows. The tool matches the asset display name, which can include the table name, database name, and schema name. The format is database:schema.table for tables, or just an ID for other asset types. Allows searching for tables, views, dashboards, reports, and other data assets in your data catalog. Results are sort…
create_comparison_monitor_mac
Create a comparison monitor (MaC - Monitors as Code) in dry-run mode. Returns YAML that can be used for monitors as code. The YAML should be used as-is, stored in a YAML structure like: montecarlo: comparison: - <returned yaml> You can add this structure in a new or existing MaC file, or under the 'meta' property of a dbt model YAML schema file.
create_custom_sql_monitor_mac
Create a custom SQL monitor (MaC - Monitors as Code) in dry-run mode. Returns YAML that can be used for monitors as code. The YAML should be used as-is, stored in a YAML structure like: montecarlo: custom_sql: - <returned yaml> You can add this structure in a new or existing MaC file, or under the 'meta' property of a dbt model YAML schema file.
create_metric_monitor_mac
What it does: Create a metric monitor (MaC - Monitors as Code) in dry-run mode. Returns YAML that can be used for monitors as code. The YAML should be used as-is, stored in a YAML structure like: montecarlo: metric: - <returned yaml> You can add this structure in a new or existing MaC file, or under the 'meta' property of a dbt model YAML schema file. How to use it: * Use the RELATIVE_ROW_COUNT metric to …
create_or_update_alert_comment
Create or update a comment on an alert.
create_table_monitor_mac
Create a table monitor (MaC - Monitors as Code) in dry-run mode. Returns YAML that can be used for monitors as code. Table monitors watch groups of tables for freshness anomalies, schema changes, and volume changes. They use asset selection to define which tables to monitor at the database/schema level. The YAML should be used as-is, stored in a YAML structure like: montecarlo: table: - <returned yaml> You can ad…
create_validation_monitor_mac
What it does: Create a validation monitor (MaC - Monitors as Code) in dry-run mode. Returns YAML that can be used for monitors as code. The YAML should be used as-is, stored in a YAML structure like: montecarlo: validation: - <returned yaml> You can add this structure in a new or existing MaC file, or under the 'meta' property of a dbt model YAML schema file. When to call it: When you want to create a val…
get_alerts
What it does: Get alerts from Monte Carlo based on specified filters. Alerts with severity assigned are called incidents. So if a user asks for incidents look for alerts with SEV_1, SEV_2, SEV_3 or SEV_4. Alerts are created by monitors when they detect an issue. Monitors and alerts are different concepts. How to use it: * Pass alert IDs to fetch specific alerts. * Pass a time range to fetch alerts created within that range. If the range exceeds 60 days, it will be auto-cl…
get_asset_lineage
What it does: Get the asset lineage information from Monte Carlo using the v4 API. Trace data flow relationships. Returns empty results if no lineage is tracked. Absence of lineage does NOT confirm absence of data usage. IMPORTANT: When has_relationships is false, this means NO dependencies are tracked in that direction - do not keep searching for non-existent relationships. DO NOT fill any gaps in the lineage with assumptions. How to paginate: * This tool uses offset-ba…
get_audiences
Get notification audiences from Monte Carlo. Audiences are used for routing notifications and grouping monitors. They help organize who should be notified about data quality issues.
get_current_time
Get the current time in ISO format compatible with Monte Carlo API tools. Returns the current UTC time in ISO 8601 format (YYYY-MM-DDTHH:MM:SS+00:00), which is compatible with date parameters used in other Monte Carlo API tools like getAlerts.
get_domains
Get domains from Monte Carlo.
get_downstream_bi_reports
What it does: Finds all downstream BI assets (Tableau workbooks/worksheets, Looker dashboards/looks, Power BI reports, etc.) that transitively depend on the given source tables. Traverses the full lineage graph (up to 40 hops) and returns only BI report nodes with structured metadata. When to use: Use this instead of getAssetLineage when you specifically need to find BI reports that depend on a table. This tool handles the full graph traversal and filtering — no need …
get_field_metric_definitions
Get valid metric names for a warehouse, optionally filtered by field type. Use this before creating metric monitors to discover which metrics are available.
get_monitors
What it does: Gets monitors from Monte Carlo based on specified filters. Returns a list of monitors with their metadata. Monitors generate alerts when they detect an issue. Monitors and alerts are different concepts. IMPORTANT: By default, the expensive 'config' field is EXCLUDED for performance. To get monitor configuration details (JSON or YAML monitor-as-code format), you MUST explicitly pass include_fields=["config"]. When to call it: When you need the configurat…
get_queries_for_table
What it does: Get query logs and execution metadata for a table between a time range, filtered by type and optional facets. When to call it: - To find out the queries that create or update a table (destination) or read from a table (source). - To find out the users that are querying a table. - To find out the performance of queries that are running against a table. What it returns: Pagination metadata and query list: - `total` (int): Total queries available …
get_query_data
What it does: Get query metadata and time-series for a specific query occurrence day. One of query_id, query_hash, or group_id is required. When to call it: After using getQueriesForTable tool to get the full query, as the query snippets returned by getQueriesForTableare truncated. How to paginate: * This tool uses offset-based pagination. Use the `offset` and `limit` parameters to paginate through results.
get_table
Get the table information (ie. schema, table name, capabilities, warehouse, stats, domains, etc.) by MCON.
get_unmonitored_tables_with_anomalies
Find unmonitored tables that have muted table monitor anomalies in a time window. Results are ordered by importance score (descending). Use this to discover monitoring coverage gaps.
get_use_case_table_summary
What it does: Get table criticality counts for a specific use case. Returns the number of tables at each criticality level (HIGH, MEDIUM, LOW). When to call it: - Get a quick overview of table distribution by criticality for a use case - Before drilling into specific tables, to understand the scope **Output Schema:** Returns a dictionary with: - `totalTablesHigh` (integer): Number of HIGH criticality tables - `totalTablesMedium` (integer): Number of MEDIUM criticality tables - `totalTablesL…
get_use_case_tables
What it does: Get all tables associated with a specific use case. Returns tables with their criticality levels, reasoning, and golden table status. **Important:** Golden tables are the most business-critical tables in a use case. Always start by returning only golden tables (golden_tables_only=true) unless the user explicitly asks for all tables. This keeps results focused on what matters most. When to call it: - List tables that belong to a specific use case (start with golden tables only)…
get_use_cases
What it does: Get all use cases for a warehouse. Use cases represent business-critical data flows and help identify monitoring opportunities for data products. Each use case includes a name, description, criticality level, and the number of associated tables. When to call it: - Discover monitoring opportunities across a warehouse - List business-critical data flows and their criticality - Get an overview of use cases before drilling into specific tables How to paginate: * This tool uses cu…
get_user
Get current user information from Monte Carlo.
get_warehouses
List all warehouses the user can access. Returns the UUID, name, and connection type for each warehouse.
get_validation_predicates
Get the list of supported predicates for validation monitors from Monte Carlo. Returns all available predicates that can be used when creating validation monitors, including their arity, supported types, SQL syntax, and compatibility information.
set_alert_owner
Set the owner for an alert. Pass an email to set, or omit owner to clear.
test_connection
Test the connection to Monte Carlo API with a simple query.
update_alert
Update an alert or incident. Only supplied fields will be updated. Declares an incident when the severity is set. Use this tool to update the status of an alert or incident or to declare an incident.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"monte carlo": {
"monte-carlo": {
"type": "http",
"url": "https://integrations.getmontecarlo.com/mcp"
}
}
}
}McpServers
{
"monte-carlo": {
"type": "http",
"url": "https://integrations.getmontecarlo.com/mcp"
}
}Monte Carlo provides data and AI observability for monitoring data reliability. Its connector gives assistants observability context for investigating data incidents, understanding quality signals, and supporting trusted analytics workflows.
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