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google_ads_cpc_detect_trend

Detect rising or falling CPC trends in Google Ads campaigns via daily segmentation and linear regression, delivering trend direction, slope, and insights to guide bid optimization.

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. Also accepts an explicit range in GAQL spelling — "BETWEEN 'YYYY-MM-DD' AND 'YYYY-MM-DD'", both endpoints inclusive, in the account's time zone — for a window no trailing constant can reach (e.g. a single past calendar month). One asymmetry to know about: every constant except LAST_90_DAYS is resolved by Google Ads in the account's reporting time zone, whereas LAST_90_DAYS has no API constant and is expanded by mureo into the 90 days ending yesterday **on the server's date**, so its edges can differ by a day when the server and the account are in different zones. Pass an explicit range when the exact boundary matters.
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.

Schema Changelog

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

  1. Changed3 schema fields changedv0.17.1
    • addedInput schema / properties / period / anyOf
      Added value: +[
      +  {
      +    "enum": [
      +      "TODAY",
      +      "YESTERDAY",
      +      "THIS_WEEK_SUN_TODAY",
      +      "THIS_WEEK_MON_TODAY",
      +      "LAST_BUSINESS_WEEK",
      +      "LAST_WEEK_SUN_SAT",
      +      "LAST_WEEK_MON_SUN",
      +      "LAST_7_DAYS",
      +      "LAST_14_DAYS",
      +      "LAST_30_DAYS",
      +      "LAST_90_DAYS",
      +      "THIS_MONTH",
      +      "LAST_MONTH"
      +    ],
      +    "type": "string"
      +  },
      +  {
      +    "pattern": "\\ABETWEEN '([0-9]{4}-[0-9]{2}-[0-9]{2})' AND '([0-9]{4}-[0-9]{2}-[0-9]{2})'\\Z",
      +    "type": "string"
      +  }
      +]
    • changedInput schema / properties / period / description
      Previous value: -"Reporting 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."New value: +"Reporting 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. Also accepts an explicit range in GAQL spelling — \"BETWEEN 'YYYY-MM-DD' AND 'YYYY-MM-DD'\", both endpoints inclusive, in the account's time zone — for a window no trailing constant can reach (e.g. a single past calendar month). One asymmetry to know about: every constant except LAST_90_DAYS is resolved by Google Ads in the account's reporting time zone, whereas LAST_90_DAYS has no API constant and is expanded by mureo into the 90 days ending yesterday **on the server's date**, so its edges can differ by a day when the server and the account are in different zones. Pass an explicit range when the exact boundary matters."
    • removedInput schema / properties / period / enum
      Removed value: -[
      -  "TODAY",
      -  "YESTERDAY",
      -  "LAST_7_DAYS",
      -  "LAST_14_DAYS",
      -  "LAST_30_DAYS",
      -  "LAST_90_DAYS",
      -  "THIS_MONTH",
      -  "LAST_MONTH"
      -]
  2. Changed1 schema field changedv0.10.37
    • addedInput schema / additionalProperties
      Added value: +false
  3. Addedv0.10.11
  4. Removedv0.10.9
  5. Addedv0.9.12
  6. Removedv0.9.6
  7. Addedv0.9.2
  8. Removedv0.9.1
  9. Addedv1.0.5

TDQS

A4.7/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses read-only status, the exact direction thresholds, zero-click day exclusion behavior, the conditions for insufficient_data, and what the insights contain. It even explains the return shape in detail, including optional fields, which is especially valuable without an output schema.

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 front-loaded with its purpose and then provides structured, dense detail about returns and behavior. The length is justified because there is no output schema and the tool has nontrivial thresholds and edge cases. Every sentence provides useful operational information.

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?

For a read-only analytical tool with three parameters and no output schema, this description is remarkably complete. It covers purpose, parameters, return structure, thresholds, exclusions, alternatives, and safety. An agent has enough context to select and invoke the tool correctly without additional inference.

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 description coverage is 100%, so the parameter descriptions already carry full semantics. The main description does not add much about individual parameters beyond what the schema provides. Baseline 3 is appropriate because the schema does the heavy lifting.

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 opens with a specific verb and resource: detect rising/falling CPC trends in a Google Ads campaign using daily segmentation and linear regression. It precisely names the object and method, and it differentiates itself from siblings by naming relevant alternatives. It is far from a tautology and gives an agent a clear picture of what the tool does.

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?

The description explicitly says to use google_ads_device_analyze or google_ads_auction_insights_analyze for device or auction-share investigation, providing a clear when-not-to-use boundary. The period parameter description adds concrete guidance on choosing shorter versus longer windows depending on the diagnostic goal. This is strong usage context beyond a mere definition.

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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