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google_ads_cpc_detect_trend

Detect rising or falling CPC trends in a Google Ads campaign using daily data and linear regression, with insights on week-over-week surges and cost anomalies.

Instructions

Detect rising/falling CPC trends in a Google Ads campaign over a reporting window using daily segmentation and linear regression. Returns {campaign_id, campaign_name, period, data_points, daily_data:[{date, average_cpc, clicks, impressions, cost}], trend:{direction ('rising'|'falling'|'stable'|'insufficient_data'), slope_per_day, change_rate_per_day_pct? (present only when direction is not 'insufficient_data' — i.e. when at least 2 daily data points are available), avg_cpc, min_cpc, max_cpc}, insights:[strings]}. Direction is 'rising' when daily change > +1%, 'falling' when < -1%. Days with zero clicks are excluded from the GAQL. Insights call out week-over-week surges >15% and days exceeding 2x average CPC. Read-only. For device or auction-share investigation use google_ads_device_analyze or google_ads_auction_insights_analyze.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
periodNoReporting window for the metrics. Default 'LAST_30_DAYS'. Use a shorter window (LAST_7_DAYS / LAST_14_DAYS) when diagnosing recent changes; use LAST_90_DAYS for trend baselines.
campaign_idYesCampaign ID as a numeric string without dashes (e.g. '23743184133'). Obtain via google_ads_campaigns_list.
customer_idNoGoogle Ads customer ID as a 10-digit string without dashes (e.g. '1234567890'). Optional — falls back to GOOGLE_ADS_CUSTOMER_ID / GOOGLE_ADS_LOGIN_CUSTOMER_ID from the configured credentials when omitted.
Behavior5/5

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

Given no annotations, the description fully carries the burden. It states read-only, explains trend direction thresholds (>+1% rising, <-1% falling), mentions exclusion of zero-click days, and describes the output structure including conditional fields like change_rate_per_day_pct. Also explains insight generation.

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

Conciseness4/5

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

The description is comprehensive and well-structured, front-loading purpose and output details. While it is long, each sentence earns its place. Minor redundancy (e.g., 'insufficient_data' condition repeated) could be trimmed, but overall effective.

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

Completeness5/5

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

No annotations or output schema, but the description compensates by clearly explaining the output structure, including nested objects and conditional fields. It covers all necessary context for an agent to invoke and interpret results.

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 coverage is 100% (all three parameters described). The description adds value beyond schema: explains fallback behavior for customer_id and suggests usage for period. It also links campaign_id to google_ads_campaigns_list for obtaining it.

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 it detects rising/falling CPC trends using daily segmentation and linear regression. It distinguishes from siblings by mentioning alternatives for device or auction-share investigation (google_ads_device_analyze, google_ads_auction_insights_analyze).

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

Usage Guidelines5/5

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

Explicitly says when to use (detect CPC trends over a reporting window) and when not to (e.g., for device or auction-share investigation, use other tools). Also provides guidance on period selection (use shorter windows for recent changes, longer for baselines).

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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