Expert BI analyst for Looker queries, explorations, visualizations, and data analysis. Integrates with Looker MCP for programmatic access. Use when building Looker queries, creating dashboards, analyzing data through Looker, generating SQL, or working with LookML models and explores.
Your expert guide for data analysis and visualization using Looker, with integrated MCP tooling for programmatic query building and dashboard creation.
The Looker MCP provides programmatic access to your Looker instance. Start every workflow by discovering available models and explores:
1. Call mcp__looker-toolbox__get_models to list available models
2. Call mcp__looker-toolbox__get_explores with a model to see available explores
3. Call mcp__looker-toolbox__get_dimensions and get_measures to discover fields
4. Build queries using the query tool
// Standard query structure
{
"model": "model_name", // From get_models
"explore": "explore_name", // From get_explores
"fields": [ // From get_dimensions/get_measures
"dimension.field_name",
"measure.field_name"
],
"filters": { // Optional
"dimension.field": "value"
},
"sorts": ["field desc 0"], // Optional
"limit": 500 // Optional, default 500
}
Goal: Understand data structure and discover insights
Steps:
Discover structure:
get_models → List available data modelsget_explores → Find relevant exploresget_dimensions + get_measures → See available fieldsBuild initial query:
query tool to executeRefine and filter:
Analyze results:
Goal: Build charts and dashboards
Steps:
Start with a query (see workflow above)
Choose visualization type:
Generate URL or create Look:
query_url tool: Get shareable Looker URL with vis_configmake_look tool: Save as permanent Look in personal folderAdd to dashboard:
add_dashboard_element to add tilesGoal: Create comprehensive dashboard with multiple tiles
Steps:
Plan dashboard structure:
Create dashboard tiles:
add_dashboard_element repeatedlyConfigure interactivity:
Goal: See underlying SQL for debugging or learning
Steps:
query_sql tool instead of queryGoal: Monitor Looker instance health and performance
Available checks:
health_pulse with actions:
check_db_connections: Verify database connectivitycheck_dashboard_performance: Find slow dashboardscheck_dashboard_errors: Identify broken dashboardscheck_explore_performance: Find slow explorescheck_schedule_failures: Check scheduled delivery failurescheck_legacy_features: Identify deprecated featureshealth_analyze: Calculate usage metrics
projects, models, explorestimeframe (days), min_querieshealth_vacuum: Suggest unused resources for cleanup
models, exploresThe Looker MCP server (looker-toolbox) provides these tools:
get_models - List all LookML modelsget_explores - List explores in a modelget_dimensions - List dimensions in an exploreget_measures - List measures in an exploreget_filters - List available filtersget_parameters - List parametersquery - Execute query and get JSON resultsquery_sql - Generate SQL without executingquery_url - Generate Looker URL with optional vis_configmake_look - Create saved Look in personal folderadd_dashboard_element - Add tile to dashboardrun_look - Execute existing Look by IDget_looks - Search for Looksget_projects - List LookML projectsdev_mode - Switch to/from development modecreate_project_file - Create LookML fileupdate_project_file - Modify LookML filedelete_project_file - Remove LookML filehealth_pulse - Run health checkshealth_analyze - Analyze usage patternshealth_vacuum - Find unused resourcesAll MCP tools are called with this naming convention:
mcp__looker-toolbox__<tool_name>
Example:
mcp__looker-toolbox__query
mcp__looker-toolbox__get_models
mcp__looker-toolbox__make_look
Filters use special syntax - see reference/filter-expressions.md for complete syntax.
Quick examples:
{
"filters": {
"orders.status": "completed,pending", // Multiple values (OR)
"orders.created_date": "7 days", // Relative date
"users.age": ">=21 AND <=65", // Number range
"products.name": "%shirt%", // Contains (wildcard)
"orders.total": "NOT NULL" // Not null
}
}
Important: Use not null (not -NULL) for null checks.
Invoke this skill when:
For detailed information, see these reference files (loaded on-demand):
These files are loaded on-demand when you need specific details.
1. get_models → find relevant model
2. get_explores → find relevant explore
3. get_dimensions + get_measures → discover fields
4. query with simple fields → validate data
5. query with filters/sorts → refine results
6. query_url with vis_config → create visualization
7. make_look → save for reuse
1. Plan: Identify 4-6 key metrics
2. For each metric:
- Build query
- Choose visualization
- call add_dashboard_element
3. Test interactivity
4. Share dashboard URL
1. health_pulse check_explore_performance → find slow explores
2. query_sql → examine generated SQL
3. Optimize: Reduce fields, add filters, check joins
4. query → validate improvement
For specific topics, tell me what you need:
I'll load the relevant reference material and guide you through the task.
##Examples
See the examples/ directory for common patterns: