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Adako: Google Ads, Meta Ads & Linkedin Ads MCP

Read ChatGPT Ads performance

chatgpt_get_performance
Read-onlyIdempotent

🟢 READ-ONLY — runs immediately, changes nothing. Cost: free (not counted against tasks).

Reports impressions, clicks, spend, conversions and the ratios derived from them (CTR, CPC, CPM, CPA, ROAS) for a window, broken down by campaign, ad group or ad — and compares every number with the equally long period immediately before it. Use when: the user asks how anything is doing, whether something is working, or what changed. Scope it with campaign_id, ad_group_id or ad_id; without any of them it reports the whole account. Do not use it for a brief or a report to keep or forward: that is generate_report_now (monitoring router), which covers every connected account and saves a page. Do not use it to find out what exists: that is chatgpt_list_campaigns. Daily numbers are noisy at small budgets, so prefer last_7_days or longer before recommending a change. The "last N days" windows end yesterday, so a partial day is never compared with a whole one. Returns spend as a decimal in the account currency and a percentage change per metric against the previous period. A dash in the change column means the previous period was zero, not that nothing changed. ROAS is only meaningful where the account reports attributed revenue; where it does not, it comes back empty rather than as zero. If the numbers are all zero, check chatgpt_list_ads for a review verdict and chatgpt_list_campaigns for a serving issue before concluding the ads are simply performing badly.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ad_idNoOnly this ad.
levelNoHow to break the numbers down: account (one total), campaign, ad_group or ad. Defaults to campaign.
limitNoMaximum rows in the breakdown (default 25, by spend).
end_dateNoLast day, YYYY-MM-DD, inclusive. Needs start_date.
raw_dataNoReturn compact JSON only (no markdown). Use when you will compute on the result.
date_rangeNoPreset window. The "last N days" ones end yesterday, so no partial day is mixed in. Either this or start_date + end_date, never both.
start_dateNoFirst day, YYYY-MM-DD. Needs end_date.
ad_group_idNoOnly this ad group.
campaign_idNoOnly this campaign.
ad_account_idNoChatGPT Ads advertiser account id. One advertiser key belongs to one account, so this is only needed when the user has connected more than one key. Omit it to use the primary account.
compare_previousNoCompare with the equally long period before the window. On by default, and still one task.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive, so the bar is lower, yet the description adds cost semantics (free, not counted against tasks), window edge behavior ("last N days" end yesterday so no partial day is mixed), the meaning of a dash in the change column, ROAS empty-vs-zero behavior, and default-on comparison. This is well beyond annotation coverage.

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 highest-value facts (read-only, free) before what it returns, then usage, then caveats. Dense but each sentence carries distinct information; slightly long for an 11-parameter read tool but nothing is clearly wasteful.

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?

No output schema exists, so the description carries return-value explanation and does so thoroughly: currency-as-decimal spend, per-metric percentage change vs previous period, dash semantics, and ROAS caveat. Combined with full routing guidance and safety annotations, an agent has everything needed to call it correctly.

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 100%, so the baseline is 3, but the description adds real meaning: it explains the three ID scoping parameters and what happens without them (whole account), reiterates the date_range vs start/end exclusivity, and confirms compare_previous defaults on. This enriches schema without fully duplicating it.

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?

States a specific verb (read/report) and resource (ChatGPT Ads performance) plus the exact metrics returned (impressions, clicks, spend, conversions, CTR/CPC/CPM/CPA/ROAS) and the breakdown levels. It clearly differentiates itself from sibling performance tools by naming the platform and from monitoring/generate_report_now explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use ("user asks how anything is doing"), when-not-to-use (not for briefs/reports → generate_report_now; not for discovery → chatgpt_list_campaigns), scoping guidance, and even a diagnostic escalation path for all-zero results. Alternatives and their selecting conditions are named.

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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