Looker MCP Server

list_measures

List all measure fields from a specified Looker explore, showing name, label, type, and description for each.

Instructions

List measures in an explore. Convenience tool that returns only the measure fields (name, label, type, description).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "model_name": "<model_name>",
      "explore_name": "<explore_name>"
    },
    "name": "list_measures"
  }
}

Input Schema

Name	Required	Description	Default
`model_name`	Yes	Name of the LookML model
`explore_name`	Yes	Name of the explore

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.20.0May 29, 2026

TDQS

A4/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the burden. It transparently states the tool returns only measure fields (name, label, type, description), which adds value beyond a generic list. However, it does not explicitly confirm read-only behavior or disclose any potential side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: two sentences, front-loaded with the core purpose, and contains no unnecessary words. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the low complexity (2 parameters, no enums, presence of output schema), the description is mostly complete. It explains the output fields but lacks explicit guidance on prerequisites, error handling, or when to prefer this tool over siblings. The presence of an output schema mitigates the need for detailed return value descriptions.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, with both parameters (model_name, explore_name) already described in the input schema. The description adds no additional meaning or constraints about these parameters, meeting the baseline for high coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists measures in an explore, specifies it's a convenience tool returning only specific measure fields (name, label, type, description), and distinguishes itself from sibling tools like list_dimensions and list_columns by focusing solely on measures.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage as a convenience tool but does not explicitly state when to use it over siblings like list_dimensions or list_columns. No guidance on prerequisites or exclusions is provided, leaving the agent to infer the context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.