Looker MCP Server

list_columns

Retrieve column metadata including names and data types for tables in a Looker connection. Specify connection, database, schema, or table to filter results.

Instructions

List columns for specific tables in a Looker connection. Returns column names, data types, and other metadata.

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": {
      "connection_name": "<connection_name>"
    },
    "name": "list_columns"
  }
}

Input Schema

Name	Required	Description
`connection_name`	Yes	Name of the Looker database connection
`database`	No	Database name
`schema_name`	No	Schema name
`table_name`	No	Table name to get columns for

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.20.0May 29, 2026

TDQS

A3.8/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It mentions returning metadata but does not disclose behavioral traits such as permissions, read-only status, or side effects. It is adequate but minimal.

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?

Two sentences, front-loaded with action and resource, no wasted words.

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 presence of an output schema, the description does not need to detail return values. However, it could mention that table_name and schema_name are optional filters. Overall, it is fairly complete for a listing tool.

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%, so baseline is 3. The description adds no extra meaning beyond the schema; it does not clarify parameter interactions or the optional nature of table filtering.

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 verb 'list' and the resource 'columns for specific tables in a Looker connection'. It mentions the returned data (column names, data types, other metadata), which distinguishes it from sibling tools like list_tables (lists tables) or list_dimensions (lists dimensions).

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 use for specific tables but does not provide explicit guidance on when to use this tool versus alternatives like list_dimensions or list_measures. No when-not-to-use or context about prerequisites is given.

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