Looker using MCP

Connect your IDE to Looker using Toolbox.

Model Context Protocol (MCP) is an open protocol for connecting Large Language Models (LLMs) to data sources like Postgres. This guide covers how to use MCP Toolbox for Databases to expose your developer assistant tools to a Looker instance:

Set up Looker

Get a Looker Client ID and Client Secret. Follow the directions here.
Have the base URL of your Looker instance available. It is likely something like https://looker.example.com. In some cases the API is listening at a different port, and you will need to use https://looker.example.com:19999 instead.

Install MCP Toolbox

Download the latest version of Toolbox as a binary. Select the correct binary corresponding to your OS and CPU architecture. You are required to use Toolbox version v0.10.0+:

curl -O https://storage.googleapis.com/genai-toolbox/v0.14.0/linux/amd64/toolbox

curl -O https://storage.googleapis.com/genai-toolbox/v0.14.0/darwin/arm64/toolbox

curl -O https://storage.googleapis.com/genai-toolbox/v0.14.0/darwin/amd64/toolbox

curl -O https://storage.googleapis.com/genai-toolbox/v0.14.0/windows/amd64/toolbox.exe

Make the binary executable:
```
chmod +x toolbox
```
Verify the installation:
```
./toolbox --version
```

Configure your MCP Client

Install Gemini-CLI.
Create a directory .gemini in your home directory if it doesn’t exist.
Create the file .gemini/settings.json if it doesn’t exist.

Add the following configuration, or add the mcpServers stanza if you already have a settings.json with content. Replace the path to the toolbox executable and the environment variables with your values, and save:

{
  "mcpServers": {
    "looker-toolbox": {
      "command": "./PATH/TO/toolbox",
      "args": ["--stdio", "--prebuilt", "looker"],
      "env": {
        "LOOKER_BASE_URL": "https://looker.example.com",
        "LOOKER_CLIENT_ID": "",
        "LOOKER_CLIENT_SECRET": "",
        "LOOKER_VERIFY_SSL": "true"
      }
    }
  }
}

Start Gemini-CLI with the gemini command and use the command /mcp to see the configured MCP tools.

Install Claude Code.
Create a .mcp.json file in your project root if it doesn’t exist.

Add the following configuration, replace the environment variables with your values, and save:

{
  "mcpServers": {
    "looker-toolbox": {
      "command": "./PATH/TO/toolbox",
      "args": ["--stdio", "--prebuilt", "looker"],
      "env": {
        "LOOKER_BASE_URL": "https://looker.example.com",
        "LOOKER_CLIENT_ID": "",
        "LOOKER_CLIENT_SECRET": "",
        "LOOKER_VERIFY_SSL": "true"
      }
    }
  }
}

Restart Claude Code to apply the new configuration.

Open Claude desktop and navigate to Settings.
Under the Developer tab, tap Edit Config to open the configuration file.

Add the following configuration, replace the environment variables with your values, and save:

{
  "mcpServers": {
    "looker-toolbox": {
      "command": "./PATH/TO/toolbox",
      "args": ["--stdio", "--prebuilt", "looker"],
      "env": {
        "LOOKER_BASE_URL": "https://looker.example.com",
        "LOOKER_CLIENT_ID": "",
        "LOOKER_CLIENT_SECRET": "",
        "LOOKER_VERIFY_SSL": "true"
      }
    }
  }
}

Restart Claude desktop.
From the new chat screen, you should see a hammer (MCP) icon appear with the new MCP server available.

Open the Cline extension in VS Code and tap the MCP Servers icon.
Tap Configure MCP Servers to open the configuration file.

Add the following configuration, replace the environment variables with your values, and save:

{
  "mcpServers": {
    "looker-toolbox": {
      "command": "./PATH/TO/toolbox",
      "args": ["--stdio", "--prebuilt", "looker"],
      "env": {
        "LOOKER_BASE_URL": "https://looker.example.com",
        "LOOKER_CLIENT_ID": "",
        "LOOKER_CLIENT_SECRET": "",
        "LOOKER_VERIFY_SSL": "true"
      }
    }
  }
}

You should see a green active status after the server is successfully connected.

Create a .cursor directory in your project root if it doesn’t exist.
Create a .cursor/mcp.json file if it doesn’t exist and open it.

Add the following configuration, replace the environment variables with your values, and save:

{
  "mcpServers": {
    "looker-toolbox": {
      "command": "./PATH/TO/toolbox",
      "args": ["--stdio", "--prebuilt", "looker"],
      "env": {
        "LOOKER_BASE_URL": "https://looker.example.com",
        "LOOKER_CLIENT_ID": "",
        "LOOKER_CLIENT_SECRET": "",
        "LOOKER_VERIFY_SSL": "true"
      }
    }
  }
}

Open Cursor and navigate to Settings > Cursor Settings > MCP. You should see a green active status after the server is successfully connected.

Open VS Code and create a .vscode directory in your project root if it doesn’t exist.
Create a .vscode/mcp.json file if it doesn’t exist and open it.

Add the following configuration, replace the environment variables with your values, and save:

{
  "servers": {
    "looker-toolbox": {
      "command": "./PATH/TO/toolbox",
      "args": ["--stdio", "--prebuilt", "looker"],
      "env": {
        "LOOKER_BASE_URL": "https://looker.example.com",
        "LOOKER_CLIENT_ID": "",
        "LOOKER_CLIENT_SECRET": "",
        "LOOKER_VERIFY_SSL": "true"
      }
    }
  }
}

Open Windsurf and navigate to the Cascade assistant.
Tap on the hammer (MCP) icon, then Configure to open the configuration file.

Add the following configuration, replace the environment variables with your values, and save:

{
  "mcpServers": {
    "looker-toolbox": {
      "command": "./PATH/TO/toolbox",
      "args": ["--stdio", "--prebuilt", "looker"],
      "env": {
        "LOOKER_BASE_URL": "https://looker.example.com",
        "LOOKER_CLIENT_ID": "",
        "LOOKER_CLIENT_SECRET": "",
        "LOOKER_VERIFY_SSL": "true"
      }
    }
  }
}

Use Tools

Your AI tool is now connected to Looker using MCP. Try asking your AI assistant to list models, explores, dimensions, and measures. Run a query, retrieve the SQL for a query, and run a saved Look.

The following tools are available to the LLM:

get_models: list the LookML models in Looker
get_explores: list the explores in a given model
get_dimensions: list the dimensions in a given explore
get_measures: list the measures in a given explore
get_filters: list the filters in a given explore
get_parameters: list the parameters in a given explore
query: Run a query and return the data
query_sql: Return the SQL generated by Looker for a query
query_url: Return a link to the query in Looker for further exploration
get_looks: Return the saved Looks that match a title or description
run_look: Run a saved Look and return the data
make_look: Create a saved Look in Looker and return the URL
get_dashboards: Return the saved dashboards that match a title or description
make_dashboard: Create a saved dashboard in Looker and return the URL
add_dashboard_element: Add a tile to a dashboard

Note

Prebuilt tools are pre-1.0, so expect some tool changes between versions. LLMs will adapt to the tools available, so this shouldn’t affect most users.

Last modified September 5, 2025: chore(main): release 0.14.0 (#1261) (4f022b670)