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meta_ads_analysis_cost

Diagnose root causes of rising spend or degrading CPA on Meta Ads campaigns. Decomposes cost change into drivers like bid increase, CPM inflation, CTR drop, CVR drop, audience saturation, or creative fatigue.

Instructions

Diagnoses root causes of rising spend or degrading CPA on a Meta Ads campaign. Returns a decomposition that attributes the cost change to drivers — bid increase, CPM inflation, CTR drop, CVR drop, audience saturation, or creative fatigue — with per-driver magnitude and a specific action hint. Read-only. Use this when the operator reports 'why did CPA jump'; it separates auction-side from creative-side causes in one call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
account_idNoMeta Ads account ID in the format 'act_XXXXXXXXXX' (e.g. 'act_1234567890'). Optional — falls back to META_ADS_ACCOUNT_ID from the configured credentials. The leading 'act_' prefix is required.
campaign_idYesCampaign to diagnose.
periodNoAnalysis window. Accepts Meta predefined ranges ('today', 'yesterday', 'last_7d', 'last_14d', 'last_30d' (default), 'last_90d', 'this_month', 'last_month') or explicit 'YYYY-MM-DD..YYYY-MM-DD' (both endpoints inclusive). Longer windows cost more Graph API quota.
Behavior4/5

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

Declares the tool as read-only and describes the return value (decomposition with per-driver magnitude and action hint). With no annotations, the description provides adequate behavioral context, though it omits details like permissions or rate limits.

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?

Four sentences with clear, front-loaded purpose and output description. Every sentence adds value, and the structure is efficient.

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?

Covers purpose, usage context, output description, and even notes Graph API quota cost. Without an output schema, the description sufficiently explains what the tool returns, making it complete for its complexity.

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%, with detailed parameter descriptions. The description does not add additional parameter information beyond the schema, meeting the baseline of 3.

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 diagnoses root causes of rising spend or degrading CPA on a Meta Ads campaign. It provides specific drivers and action hints, distinguishing it from generic analytics tools.

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?

Explicitly says to use when the operator reports 'why did CPA jump' and notes it separates auction-side from creative-side causes. However, it does not mention when not to use or explicitly differentiate from sibling analysis tools.

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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