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niyogi

chatgpt-ads-manager-mcp

by niyogi

get_ad_group_insights

Retrieve ad group performance data by ad group ID, with options for filters, segments, and time ranges to analyze impressions, clicks, and spend.

Instructions

Get ad group insights. Chain from list_ad_groups -> ad_group_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNoSort specs, e.g., ["-impressions"].
afterNoCursor for pagination.
limitNoMax rows 1–2000.
beforeNoCursor for pagination.
fieldsNoFields to return, e.g., ["impressions","clicks","spend_micros"].
filtersNoFilters array (opaque, enumerated in docs).
includesNo
segmentsNoSegments: product, country, device, etc. For product breakdown add product.feed_id + product.item_id to fields.
ad_group_idYesAd group ID.
time_rangesNoTime ranges, e.g., ["2026-01-01:2026-01-31"]. Check docs for format; required for conversion insights.
ad_account_idNo
time_granularityNoTime bucket. Use daily for trend analysis.
aggregation_levelNoAggregation scope.
override_segment_group_orderNo
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure, and it reveals nothing beyond the basic operation. It does not mention pagination cursors, limits, sorting, filtering, segmentation behavior, or the fact that time_ranges is required for conversion insights. The agent gets no safety or behavior context.

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?

Two short sentences with the core scoping statement front-loaded and zero filler. The chaining instruction is efficient and earns its place. The terseness is borderline under-specification rather than deliberate density, but structurally this is clean and scannable.

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

Completeness2/5

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

For a tool with 14 parameters, no output schema, and no annotations, the description is far too thin. An agent gets no sense of which metrics are returned, how pagination works, what behavioral caveats apply, or how this differs from the other three insights tools. The burden shifts entirely to the parameter schema, which is insufficient for confident selection.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 79%, so the schema documents most parameters. The description adds real value for the one required parameter by specifying that ad_group_id should come from list_ad_groups, which is more meaningful than the schema's generic 'Ad group ID.' This provenance hint compensates for the few undocumented parameters and helps the agent assemble a correct call.

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 verb 'Get' plus resource 'ad group insights' states the operation precisely, and the ad-group scope differentiates it from sibling tools like get_campaign_insights, get_ad_insights, and get_ad_account_insights. The chaining hint reinforces the target resource. It is clear and specific, though it does not name sibling alternatives explicitly as the strongest definitions do.

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 'Chain from list_ad_groups -> ad_group_id' line gives a concrete prerequisite and data source for the required parameter, providing implied usage guidance. However, it does not state when to choose this over the sibling insights tools, nor does it mention any exclusions or conditions, leaving the choice largely to inference.

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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