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get_advertiser_report

[Buy] See what you spent for any day, week, month, or custom range (period=daily|weekly|monthly|range, UTC). Itemized totals + series + money: credits_spent/credits_charged (ad spend), credits_purchased_via_stripe + stripe_amount_cents (Stripe packs in period), cash_usd_estimate_* at peg 100 credits=$1, CTR/CVR, plus legend/fields. credits_earned is always 0 on buy side. Analytics warehouse (~15m lag). For one-campaign postbacks use get_attribution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNoYYYY-MM-DD inclusive -- range mode
dateNoYYYY-MM-DD -- daily mode (default yesterday UTC)
monthNoYYYY-MM -- monthly mode (default current month to date)
startNoYYYY-MM-DD inclusive -- range mode
periodYesReporting window mode
week_startNoYYYY-MM-DD Monday -- weekly mode (default current ISO week to date)
campaign_idNoOptional campaign filter (your campaigns only).
placement_idNoOptional placement filter.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed5 schema fields changed
    • changedInput schema / properties / date / description
      Previous value: -"YYYY-MM-DD — daily mode (default yesterday UTC)"New value: +"YYYY-MM-DD -- daily mode (default yesterday UTC)"
    • changedInput schema / properties / end / description
      Previous value: -"YYYY-MM-DD inclusive — range mode"New value: +"YYYY-MM-DD inclusive -- range mode"
    • changedInput schema / properties / month / description
      Previous value: -"YYYY-MM — monthly mode (default current month to date)"New value: +"YYYY-MM -- monthly mode (default current month to date)"
    • changedInput schema / properties / start / description
      Previous value: -"YYYY-MM-DD inclusive — range mode"New value: +"YYYY-MM-DD inclusive -- range mode"
    • changedInput schema / properties / week_start / description
      Previous value: -"YYYY-MM-DD Monday — weekly mode (default current ISO week to date)"New value: +"YYYY-MM-DD Monday -- weekly mode (default current ISO week to date)"
  2. Added

TDQS

A3.9/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses the ~15m analytics lag, states 'credits_earned is always 0 on buy side', clarifies UTC timezone, and explains the cash peg (100 credits=$1). These are meaningful behavioral traits beyond simply describing a report retrieval.

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 dense but front-loaded, starting with the core purpose and then enumerating key output fields. Every sentence carries information about semantics, lag, or usage. While it is long, it is well-structured and scannable enough for an agent to extract essential facts 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?

Given there is no output schema, the description compensates by listing return fields (credits_spent, stripe amounts, cash estimates, CTR/CVR) and explaining key behaviors like credits_earned always 0 and the 15m lag. It could be more complete regarding the shape of the 'series' and error/empty cases, but it is largely sufficient.

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%, so the baseline is 3. The description adds some context by listing period values inline ('daily|weekly|monthly|range') and noting UTC, but it doesn't add substantial meaning beyond what the schema already describes for each parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'See what you spent for any day, week, month, or custom range'. It identifies the resource as advertiser spend and mentions a specific alternative (get_attribution) for one-campaign postbacks. However, it does not explicitly distinguish itself from the sibling tools get_advertiser_report_by_campaign and get_advertiser_report_by_placement.

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?

The description provides an explicit usage alternative: 'For one-campaign postbacks use get_attribution.' It also gives context about the analytics warehouse lag (~15m), implying it is not real-time. It doesn't, however, provide guidance on when to use this tool versus the by_campaign/by_placement report variants.

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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