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    Lookerctl

    A lightweight CLI and MCP server for automating Looker workflows, CI pipelines, and AI-assisted LookML development.

    2 stars
    Python
    Updated Oct 17, 2025

    Table of Contents

    • Quick Links
    • Quick Start
    • Key Features
    • Documentation
    • Getting Started
    • Reference
    • Examples
    • Example Workflows
    • Local Development
    • AB Testing Optimization
    • Usage Analysis
    • MCP Server for AI Agents
    • Installation
    • Performance
    • Architecture
    • Key Commands
    • Use Cases
    • For Developers
    • For AI Systems
    • For Teams
    • Support
    • Status

    Table of Contents

    • Quick Links
    • Quick Start
    • Key Features
    • Documentation
    • Getting Started
    • Reference
    • Examples
    • Example Workflows
    • Local Development
    • AB Testing Optimization
    • Usage Analysis
    • MCP Server for AI Agents
    • Installation
    • Performance
    • Architecture
    • Key Commands
    • Use Cases
    • For Developers
    • For AI Systems
    • For Teams
    • Support
    • Status

    Documentation

    lookerctl

    Comprehensive CLI for managing and optimizing LookML at scale

    Quick Links

    📚 **Complete Documentation** - Start here for full documentation

    Quick Start

    bash
    # 1. Setup environment
    source .env
    
    # 2. Test connection
    lookerctl test
    
    # 3. Export LookML
    lookerctl export all --output-dir ./lookml
    
    # 4. Perform static analysis and validation (fast!)
    lookerctl validate static ./lookml
    
    # 5. Analyze usage
    lookerctl analyze usage

    Key Features

    • ⚡ 66x Faster Validation - Local validation in 600ms vs 40s with API for a large-scale looker instance
    • 🔄 Complete Local Development - Export → Edit → Validate → Push workflow
    • 📊 Usage Analysis - Understand which fields users actually use
    • 🧪 Scientific Testing - AB testing and golden tests for confidence, complementing Looker's built-in data tests
    • 🔍 Dependency Mapping - Understand impact before making changes
    • 🤖 AI-Ready - All outputs in JSON for AI consumption
    • 🔌 MCP Server - 20 tools for AI agents via Model Context Protocol

    Documentation

    Getting Started

    • **Getting Started Guide** - Installation and setup
    • **Core Concepts** - Understanding the tool

    Reference

    • **CLI Reference** - Complete command reference
    • **Query Testing** - Baselines and AB testing
    • **Local Validation** - Fast local validation
    • **AI Integration** - AI-driven workflows
    • **MCP Server** - Model Context Protocol integration for AI agents

    Examples

    • **Examples Directory** - Real working examples and templates

    Example Workflows

    Local Development

    bash
    # Sync with local git branch
    lookerctl session sync my-project
    
    # Export to local
    lookerctl export project my-project -o ./local
    
    # Make changes
    vim ./local/views/users.view.lkml
    
    # Validate (600ms!)
    lookerctl validate static ./local
    
    # Push to Looker
    lookerctl export push my-project ./local

    AB Testing Optimization

    bash
    # Create baseline BEFORE changes
    lookerctl query-test baseline create model.explore \
      -q query.json -o before.json
    
    # Make optimization changes
    # ... optimize SQL, add indexes ...
    
    # Compare AFTER changes
    lookerctl query-test baseline compare model.explore \
      -b before.json
    
    # Result: ✓ 39% faster with no data changes!

    Usage Analysis

    bash
    # Analyze what's being used
    lookerctl analyze usage -o usage.json
    
    # Find unused explores
    lookerctl analyze unused
    
    # Build dependency graph
    lookerctl deps build ./lookml -o deps.json

    MCP Server for AI Agents

    bash
    # Start MCP server (for Claude Desktop)
    lookerctl mcp
    
    # Add to Claude Desktop config:
    # {
    #   "mcpServers": {
    #     "looker": {
    #       "command": "/path/tlookerctl",
    #       "args": ["mcp"],
    #       "env": { ... }
    #     }
    #   }
    # }
    
    # Then ask Claude:
    # "List all Looker projects"
    # "Find unused explores"
    # "Analyze dependencies in ./lookml"

    Installation

    bash
    # 1. Install dependencies
    uv pip install -r requirements.txt
    
    # 2. Configure credentials (.env file)
    export LOOKERSDK_BASE_URL=https://your-instance.looker.com:443
    export LOOKERSDK_CLIENT_ID=your_client_id
    export LOOKERSDK_CLIENT_SECRET=your_client_secret
    
    # 3. Test connection
    lookerctl test

    Performance

    OperationAPI MethodLocal GraphSpeedup
    Validate project40s600ms66x faster
    Field discovery2s100ms20x faster
    Dependency check5s50ms100x faster

    Architecture

    code
    TODO: populate architecture diagram

    Key Commands

    bash
    # Session management
    lookerctl session sync
    lookerctl session workspace dev
    
    # Export/Push
    lookerctl export all -o ./lookml
    lookerctl export push my-project ./lookml
    
    # Validation
    lookerctl validate static ./lookml     # 600ms
    lookerctl validate local my-project ./lookml   # 40s, accurate
    
    # Analysis
    lookerctl analyze usage
    lookerctl deps build ./lookml
    
    # Testing
    lookerctl query-test baseline create model.explore -q query.json
    lookerctl query-test baseline compare model.explore -b baseline.json
    lookerctl query-test golden run

    Use Cases

    For Developers

    • Local development workflow like Looker console
    • Fast validation during iteration
    • Safe refactoring with baselines
    • Impact analysis before changes

    For AI Systems

    • MCP server with 20 tools for AI agents
    • Export all LookML for analysis
    • Programmatic field discovery
    • Automated optimization with validation
    • Scientific testing of improvements

    For Teams

    • Visibility into user-created content
    • CI/CD integration with golden tests
    • Performance tracking over time
    • Automated optimization pipelines

    Support

    • 📖 Documentation: See docs/
    • 🐛 Issues: Report via your team's issue tracker
    • ❓ Help: Start with Getting Started

    Status

    ✅ Production Ready

    • All features tested end-to-end
    • Complete AB testing workflow validated
    • 66x faster local validation
    • Comprehensive documentation
    • Full error handling

    ---

    Version: 0.1.0

    Last Updated: 2025-10-11

    For complete documentation, see: docs/README.md

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