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Opus Growth, Ad Platforms MCP Connector

ChatGPT Ads Object Details

chatgpt_ads_details
Read-onlyIdempotent

Full details of one ChatGPT Ads campaign, ad group or ad by id, including everything that decides delivery.

Campaign (cmpn_…): budget, schedule, objective, full location/platform/audience targeting, attached conversion events, serving issues. Ad group (adgrp_…): bid strategy and bid in the account currency, bid_too_low (with the 24-hour caveat: OpenAI does not raise it within 24 hours of an ad-group edit, so the time it becomes evaluable is returned), serving issues, its parent campaign and the context hints (paged by hints_limit/hints_offset). Ad (ad_…): the full creative — title, body, destination URL, image — plus review status, the review reason (for example a regulated health category) and serving issues with plain explanations. Read-only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
accountNo
hints_limitNo
hints_offsetNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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TDQS

A4.1/5.0
Behavior4/5

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

Annotations already cover the read-only/idempotent/non-destructive profile, so the bar is lower. The description still adds real value beyond them: the entity-type field inventory, pagination behavior for context hints, and especially the 24-hour bid_too_low caveat explaining when the value becomes evaluable.

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?

Front-loaded with the core purpose before the per-entity breakdown, and the structure is scannable. It is on the long side and 'including everything that decides delivery' is slightly padded, but every substantive clause (prefixes, caveat, paging) earns its place.

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?

An output schema exists, so return-value detail is not strictly required, yet the description usefully previews it. Given four parameters at 0% schema coverage, the only real gap is the unexplained `account` parameter; otherwise the definition is complete for correct invocation.

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 description coverage is 0%, so the description must carry the load. It does well for id (revealing the cmpn_/adgrp_/ad_ prefix convention, which is critical for correct invocation) and for hints_limit/hints_offset paging, but the `account` parameter is never mentioned, leaving one of four parameters undocumented.

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 states a specific verb and resource ('Full details of one ChatGPT Ads campaign, ad group or ad by id') and enumerates exactly what 'details' means for each of the three id prefixes. This cleanly separates it from list_*/create_*/update_chatgpt_ads_* siblings without needing to name them.

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?

Usage is implied by 'full details of one ... by id' — the agent can infer it is a single-object read. But no explicit when-to-use guidance or alternatives (e.g., chatgpt_ads_status, chatgpt_ads_insights) are named, leaving the agent to guess which read tool to pick.

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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