TrueClicks

Perfmon alert output table

perfmon_alert_output_table

Read-only

Get the per-campaign metric rows behind a triggered perfmon alert as JSONL. The alert is identified by its PerfmonId and PerfmonDefinitionMetricId (both from a perfmon alert row) plus the accountId. Each row is one campaign for one date range; the DateRange column is 1 = current period, 2 = previous period (for fluctuation metrics). Use campaignIds to limit to specific campaigns.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Optional maximum number of rows to return, applied after the campaign filter. Defaults to 10000 when omitted.
`accountId`	Yes	Single TrueClicks account id the alert belongs to (the numeric Id field from the account listing, not the platform customer id).
`perfmonId`	Yes	Perfmon definition id, from the PerfmonId field in a perfmon alert view
`campaignIds`	No	Optional list of campaign IDs to filter rows. Omit or leave empty to return all campaigns.
`perfmonDefinitionMetricId`	Yes	Perfmon definition metric id, from the PerfmonDefinitionMetricId field in a perfmon alert view

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed1 schema field changedSep 7, 2026

changedInput schema / properties / accountId / description

Previous value: -"Single account Id the alert belongs to"New value: +"Single TrueClicks account id the alert belongs to (the numeric Id field from the account listing, not the platform customer id)."

First observedSep 7, 2026

TDQS

A4.2/5.0

Behavior4/5

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

The description adds useful behavioral context beyond the read-only annotation: it specifies the output format (JSONL), the row structure (one per campaign per date range), and the meaning of the DateRange column. Since annotations already indicate read-only and non-destructive behavior, the additional detail is appropriately supplementary.

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 with no redundancy. The main purpose is front-loaded, followed by precise identification details and output semantics. Every clause adds value; no filler or unnecessary repetition.

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?

For a tool with five parameters and no output schema, the description gives sufficient context: purpose, identification method, output format, row granularity, DateRange meaning, and filtering option. It does not enumerate possible output fields, but that is not essential for invoking the tool correctly.

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 each parameter already described. The description restates where PerfmonId and PerfmonDefinitionMetricId come from (matching schema text) and clarifies the campaignIds filter, but these are marginal additions. The primary new information (DateRange) pertains to the output, not parameters. Baseline of 3 is appropriate given high schema 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?

Clearly states the action ('Get') and the resource ('per-campaign metric rows behind a triggered perfmon alert'), specifying the output format (JSONL). Distinguishes from sibling alert-view tools by indicating this returns the underlying rows, not the alert definitions.

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

Usage Guidelines4/5

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

Provides explicit instructions on how to identify the alert (PerfmonId, PerfmonDefinitionMetricId, accountId) and how to filter (campaignIds). Explains the DateRange semantics for interpreting rows. While it doesn't explicitly contrast with sibling tools, the context of sibling names (perfmon_fluctuation_alert, perfmon_threshold_alert) makes the use case clear.

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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TDQS

A3.9/5.0

Disambiguation4/5

Most tools have clearly distinct purposes, but the audit and report tools (e.g., audit_rule_output_table vs audit_rule_output_table_historical vs audit_rule_result vs audit_rule_result_campaign) are closely related and rely on detailed descriptions to differentiate.

Naming Consistency4/5

Naming is generally consistent with snake_case and domain-prefixed groups (google_ads_*, ms_ads_*, perfmon_*), though a few tools are bare nouns (account, task, user) rather than verb-led, which slightly deviates from the dominant pattern.

Tool Count4/5

23 tools is on the higher end but justified for a platform covering multiple ad platforms, audit reports, alerts, and user management; it remains navigable with clear groupings.

Completeness4/5

The tool set comprehensively covers account enumeration, audit results, report execution for major ad platforms, perfmon alerts, pacing targets, tasks, and users. Minor gaps exist (e.g., no create/update/modify operations), but the core analytics and monitoring surface is well covered.

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