Files
genai-toolbox/docs/LOOKER_README.md
Averi Kitsch 86f374c631 docs: update Antigravity docs (#2025)
## Description

> Should include a concise description of the changes (bug or feature),
it's
> impact, along with a summary of the solution

## PR Checklist

> Thank you for opening a Pull Request! Before submitting your PR, there
are a
> few things you can do to make sure it goes smoothly:

- [ ] Make sure you reviewed

[CONTRIBUTING.md](https://github.com/googleapis/genai-toolbox/blob/main/CONTRIBUTING.md)
- [ ] Make sure to open an issue as a

[bug/issue](https://github.com/googleapis/genai-toolbox/issues/new/choose)
  before writing your code! That way we can discuss the change, evaluate
  designs, and agree on the general idea
- [ ] Ensure the tests and linter pass
- [ ] Code coverage does not decrease (if any source code was changed)
- [ ] Appropriate docs were updated (if necessary)
- [ ] Make sure to add `!` if this involve a breaking change

🛠️ Fixes #<issue_number_goes_here>

---------

Co-authored-by: prernakakkar-google <158031829+prernakakkar-google@users.noreply.github.com>
2025-11-25 19:26:15 +00:00

5.7 KiB

Looker MCP Server

The Looker Model Context Protocol (MCP) Server gives AI-powered development tools the ability to work with your Looker instance. It supports exploring models, running queries, managing dashboards, and more.

Features

An editor configured to use the Looker MCP server can use its AI capabilities to help you:

  • Explore Models - Get models, explores, dimensions, measures, filters, and parameters
  • Run Queries - Execute Looker queries, generate SQL, and create query URLs
  • Manage Dashboards - Create, run, and modify dashboards
  • Manage Looks - Search for and run saved looks
  • Health Checks - Analyze instance health and performance

Prerequisites

  • Download and install MCP Toolbox:

    1. Download the Toolbox binary: Download the latest binary for your operating system and architecture from the storage bucket. Check the releases page for additional versions:

      • To install Toolbox as a binary on Linux (AMD64):

        curl -L -o toolbox https://storage.googleapis.com/genai-toolbox/v0.21.0/linux/amd64/toolbox
        
      • To install Toolbox as a binary on macOS (Apple Silicon):

        curl -L -o toolbox https://storage.googleapis.com/genai-toolbox/v0.21.0/darwin/arm64/toolbox
        
      • To install Toolbox as a binary on macOS (Intel):

        curl -L -o toolbox https://storage.googleapis.com/genai-toolbox/v0.21.0/darwin/amd64/toolbox
        
      • To install Toolbox as a binary on Windows (AMD64):

        curl -o toolbox.exe "https://storage.googleapis.com/genai-toolbox/v0.21.0/windows/amd64/toolbox.exe"
        
    2. Make it executable:

      chmod +x toolbox
      
    3. Add the binary to $PATH in .~/bash_profile (Note: You may need to restart Antigravity for changes to take effect.):

      export PATH=$PATH:path/to/folder
      

      On Windows, move binary to the WindowsApps\ folder:

      move "C:\Users\<path-to-binary>\toolbox.exe" "C:\Users\<username>\AppData\Local\Microsoft\WindowsApps\"
      

      Tip: Ensure the destination folder for your binary is included in your system's PATH environment variable. To check PATH, use echo $PATH (or echo %PATH% on Windows).

  • Access to a Looker instance.

  • API Credentials (Client ID and Client Secret) or OAuth configuration.

Install & Configuration

  1. In the Antigravity MCP Store, click the "Install" button.

  2. Add the required inputs for your instance in the configuration pop-up, then click "Save". You can update this configuration at any time in the "Configure" tab.

You'll now be able to see all enabled tools in the "Tools" tab.

Usage

Once configured, the MCP server will automatically provide Looker capabilities to your AI assistant. You can:

  • "Find explores in the 'ecommerce' model."
  • "Run a query to show total sales by month."
  • "Create a new dashboard named 'Sales Overview'."

Server Capabilities

The Looker MCP server provides a wide range of tools. Here are some of the key capabilities:

Tool Name Description
get_models Retrieves the list of LookML models.
get_explores Retrieves the list of explores defined in a LookML model.
query Run a query against the LookML model.
query_sql Generate the SQL that Looker would run.
run_look Runs a saved look.
run_dashboard Runs all tiles in a dashboard.
make_dashboard Creates a new dashboard.
add_dashboard_element Adds a tile to a dashboard.
health_pulse Checks the status of the Looker instance.
dev_mode Toggles development mode.
get_projects Lists LookML projects.

Custom MCP Server Configuration

The MCP server is configured using environment variables.

export LOOKER_BASE_URL="<your-looker-instance-url>"  # e.g. `https://looker.example.com`. You may need to add the port, i.e. `:19999`.
export LOOKER_CLIENT_ID="<your-looker-client-id>"
export LOOKER_CLIENT_SECRET="<your-looker-client-secret>"
export LOOKER_VERIFY_SSL="true" # Optional, defaults to true
export LOOKER_SHOW_HIDDEN_MODELS="true" # Optional, defaults to true
export LOOKER_SHOW_HIDDEN_EXPLORES="true" # Optional, defaults to true
export LOOKER_SHOW_HIDDEN_FIELDS="true" # Optional, defaults to true

Add the following configuration to your MCP client (e.g., settings.json for Gemini CLI, mcp_config.json for Antigravity):

{
  "mcpServers": {
    "looker": {
      "command": "toolbox",
      "args": ["--prebuilt", "looker", "--stdio"],
      "env": {
        "LOOKER_BASE_URL": "https://your.looker.instance.com",
        "LOOKER_CLIENT_ID": "your-client-id",
        "LOOKER_CLIENT_SECRET": "your-client-secret"
      }
    }
  }
}

Documentation

For more information, visit the Looker documentation.