Skip to main content
Glama
HYPD-AI

HYPD AI - OpenAI Ads

by HYPD-AI

Get ad account insights

get_account_insights
Read-only

Retrieve aggregated ad account performance insights across campaigns, ad groups, or ads. Filter by date range, sort by metrics like clicks or spend, and page through results.

Instructions

Retrieve performance insights aggregated across the entire ad account. Returns a list response (data[] with first_id/last_id/has_more for paging). Each row carries id, start_time, end_time, plus the projected fields such as impressions, clicks, spend, ctr, cpc, cpm, readable_time, campaign_name, ad_group_name, and ad_name. Combine aggregation_level, sort, and limit to rank entities (e.g. the top ad by clicks). Monetary metrics (spend, cpc, cpm) are in the account's currency as decimal values, not micros.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sinceNoStart date of the reporting window (inclusive), YYYY-MM-DD. Combined with `until` into a date_range time filter.
untilNoEnd date of the reporting window (inclusive), YYYY-MM-DD. Combined with `since` into a date_range time filter.
time_granularityNoAggregation bucket size: 'daily' for one row per day, or 'none' for a single aggregated row over the whole window.
aggregation_levelNoScope each row is aggregated to (e.g. 'ad' to break results out per ad even when querying a campaign). Combine with `sort` + `limit` to rank entities.
fieldsNoFields to project in each row, e.g. ['ad_id','ad_name','campaign_name','readable_time','impressions','clicks','spend','ctr','cpc','cpm'].
sortNoSort expressions applied in order, e.g. [{ "field": "clicks", "direction": "desc" }] to rank by most clicks.
filtersNoAdvanced filter expressions, passed through to the API as-is.
limitNoMaximum number of rows to return (1-10000).
afterNoPagination cursor: pass `last_id` from a previous page to fetch the next page.
beforeNoPagination cursor: pass `first_id` from a previous page to fetch the previous page.
Behavior5/5

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

Annotations are readOnlyHint and openWorldHint. The description adds value beyond annotations by detailing the list response format with paging (first_id, last_id, has_more), mentioning monetary metrics are decimal (not micros) in account currency, and listing typical fields. No contradiction.

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 detailed but well-structured: front-loads purpose, then response format, then usage hint. Each sentence adds value, though slightly verbose. It efficiently communicates key information.

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

Completeness5/5

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

Given no output schema, the description thoroughly explains return format (paging, fields, currency), parameter combinations, and filtering. Covers all 10 parameters with practical guidance, making it complete for effective tool usage.

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 has 100% coverage with descriptions for all 10 parameters. The description adds extra context by explaining the combined use of aggregation_level, sort, and limit for ranking, and clarifies monetary metric format (decimal vs micros), providing value beyond schema.

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 retrieves performance insights aggregated across the entire ad account, distinguishing from sibling tools like get_campaign_insights by specifying 'across the entire ad account'. It also details the response structure and available fields.

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 explains how to combine aggregation_level, sort, and limit to rank entities (e.g., top ad by clicks), and mentions paging. It implies when to use this tool for account-level analysis, but does not explicitly state when not to use it or directly contrast with siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/HYPD-AI/openai-ads-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server