dbtBigQuery

list_metrics

List all metrics defined in the semantic layer, with optional substring filtering by name, label, or description. Returns each metric's name, label, type, one-line description, and short definition for governed GA4 metrics.

Instructions

List every metric the semantic layer defines.

Args:
    search: Optional case-insensitive substring to filter metric names,
        labels and descriptions. Leave empty to list all.

Returns the metric name, label, type, one-line description and the short
definition. Use `describe_metric` for the full governance block.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`search`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 21, 2026

TDQS

A4.1/5.0

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 of behavioral disclosure. It states what is returned (metric name, label, type, description, short definition) and implies a read-only operation, but it does not disclose potential limitations like result size, pagination, or permission requirements. The behavior is clear but not exhaustive.

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 concise and well-structured: a one-sentence purpose, a clear args explanation, and a return summary. It front-loads the primary action and includes no redundant text, making it easy for an agent to parse quickly.

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?

The description is complete for a list tool: it explains the search parameter, the return fields, and points to describe_metric for deeper detail. An output schema exists, so the description need not elaborate the exact structure. Minor gaps like pagination or maximum results are not mentioned, but given the simplicity, it is largely sufficient.

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

Parameters5/5

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

Schema description coverage is 0%, but the description fully compensates by explaining the search parameter: it is optional, case-insensitive, and filters on metric names, labels, and descriptions, with 'Leave empty to list all' clarifying the default. This adds significant meaning beyond the schema's bare type and default.

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's purpose with a specific verb and resource: 'List every metric the semantic layer defines.' It distinguishes itself from siblings like list_semantic_models (which lists models) and describe_metric (which provides full governance details), and the mention of describe_metric further clarifies scope.

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 provides partial usage guidance by pointing to describe_metric for full governance details, implying this tool gives short definitions. However, it does not explicitly compare against other siblings like list_dimensions or query_metrics, nor does it state when not to use this tool. This is adequate but not comprehensive.

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

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