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ZLeventer

google-ads-mcp

gads_auction_insights

Retrieve auction insights comparing impression share, overlap rate, outranking share, top-of-page percentage, and absolute top percentage against competitors. Optionally filter by campaign.

Instructions

Auction Insights report: impression share, overlap rate, outranking share, top-of-page %, and absolute top % vs competitors. Optional campaign filter.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
customer_idNoOverride GOOGLE_ADS_CUSTOMER_ID for this call
start_dateNoStart date: YYYY-MM-DD, NdaysAgo, yesterday, or today28daysAgo
end_dateNoEnd date: YYYY-MM-DD, NdaysAgo, yesterday, or todayyesterday
campaign_idNoFilter to a specific campaign ID

Implementation Reference

  • src/index.ts:204-209 (registration)
    Registers the 'gads_auction_insights' tool on the MCP server, linking it to auctionInsightsSchema and the auctionInsights handler function.
    server.tool(
      "gads_auction_insights",
      "Auction Insights report: impression share, overlap rate, outranking share, top-of-page %, and absolute top % vs competitors. Optional campaign filter.",
      auctionInsightsSchema,
      async (args) => { try { return ok(await auctionInsights(args)); } catch (e) { return err(e); } }
    );
  • Defines auctionInsightsSchema with base args (customer_id, start_date, end_date) and optional campaign_id filter.
    export const auctionInsightsSchema = {
      ...baseArgs,
      campaign_id: z.string().optional().describe("Filter to a specific campaign ID"),
    };
  • Implements auctionInsights handler, executing a GAQL query against the auction_insight view to return competitor auction metrics (domain, impression share, overlap rate, outranking share, top/absolute top %).
    export async function auctionInsights(args: z.infer<z.ZodObject<typeof auctionInsightsSchema>>) {
      const customer = getCustomer(args.customer_id);
      const start = resolveDate(args.start_date);
      const end = resolveDate(args.end_date);
      const campaignClause = args.campaign_id ? `AND campaign.id = ${args.campaign_id}` : "";
      const rows = await customer.query(`
        SELECT
          auction_insight.domain,
          segments.auction_insight_domain,
          campaign.id,
          campaign.name,
          metrics.auction_insight_search_impression_share,
          metrics.auction_insight_search_overlap_rate,
          metrics.auction_insight_search_outranking_share,
          metrics.auction_insight_search_position_above_rate,
          metrics.auction_insight_search_top_impression_percentage,
          metrics.auction_insight_search_absolute_top_impression_percentage
        FROM auction_insight
        WHERE segments.date BETWEEN '${start}' AND '${end}'
          ${campaignClause}
        ORDER BY metrics.auction_insight_search_impression_share DESC
        LIMIT 200
      `);
      return { rowCount: rows.length, rows };
    }
  • Imports auctionInsights and auctionInsightsSchema from src/tools/performance.ts.
    import {
      auctionInsights,
      auctionInsightsSchema,
Behavior2/5

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

No annotations provided, so description must cover behavioral traits. It does not mention data freshness, permissions, rate limits, or side effects. It only lists returned metrics, lacking needed transparency for a read operation.

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?

Single concise sentence effectively communicates the tool's output. Could be slightly improved by front-loading key info, but current structure is clear and efficient.

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

Completeness3/5

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

Simple tool with schema covering all params. Missing details about return format (e.g., rows, columns) and potential grouping. Though no output schema exists, description should clarify what the report structure looks like.

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 has 100% coverage on parameter descriptions. Description adds 'Optional campaign filter' which is already documented in the schema. No additional semantic value beyond what the schema provides.

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?

Description explicitly states the tool returns Auction Insights report with specific metrics (impression share, overlap rate, outranking share, top-of-page %, absolute top %) and an optional campaign filter. This clearly distinguishes it from sibling tools like gads_impression_share or gads_keyword_performance.

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?

No guidance on when to use this tool versus alternatives. It simply states what it does, leaving the agent to infer usage context from the metrics listed.

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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