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meta join product insights

meta_join_product_insights

Read-onlyIdempotent

Query Meta Ads product-breakdown insights and enrich each row with Product Catalog metadata for deeper product-level analysis.

Instructions

Query product-breakdown insights and enrich rows with Product Catalog metadata when catalog access is available.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`level`	No	Entity or aggregation level for the requested report.	ad
`limit`	No	Maximum number of returned items or rows. The declared bounds and default apply; use pagination for additional results.
`metrics`	No	Native or documented calculated metric names to request. Check compatibility with the selected dimensions.
`catalogId`	No	Product Catalog ID used to load product metadata for joins.
`timeRange`	No	Optional custom insights date range.
`datePreset`	No	Insights date preset. Defaults to last_30d.
`adAccountId`	Yes	Ad account ID (e.g., act_123456789)
`productBreakdown`	No	Native product grouping used to join catalog items with reported insights.	product_id
`catalogProductLimit`	No	Maximum catalog product records read for the join.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes	Original tool result: parsed JSON when the text is JSON, otherwise the text or multiple MCP content blocks. Provider fields depend on the selected query.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.0.1Sep 23, 2026

TDQS

A3.5/5.0

Behavior3/5

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

Annotations already declare the tool read-only, idempotent, and non-destructive, so the safety profile is covered. The description adds useful context about conditional enrichment with Product Catalog metadata, but it does not explain what happens if catalog access is unavailable, whether enrichment is best-effort, or how the join affects returned rows.

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 a single sentence that front-loads the main action and contains no filler. Every word contributes to conveying the tool's purpose and the conditional enrichment behavior.

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?

The rich input schema, output schema, and annotations cover most operational detail, so the description does not need to repeat them. However, the tool is complex with 9 params, and the description leaves the conditional catalog-access behavior vague and omits guidance on selecting this tool over sibling insight tools, which is a meaningful gap.

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 schema already explains every parameter. The description does not add parameter-level detail beyond the concepts of 'product-breakdown insights' and 'Product Catalog metadata', which map to existing params but add no new semantic value. Baseline 3 applies.

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 identifies the tool's operation: 'Query product-breakdown insights and enrich rows with Product Catalog metadata'. This distinguishes it from general insights tools by specifying the product-breakdown dimension and catalog join. However, it does not explicitly mention or differentiate from sibling tools like meta_get_insights or meta_get_catalog_products.

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?

The description implies a use case: when product-level insights are needed and catalog metadata is accessible. The phrase 'when catalog access is available' gives some context, but there is no explicit comparison to alternatives, no when-not-to-use guidance, and no mention of when the simpler meta_get_insights would be preferred.

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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