chatgpt-ads-mcp

An MCP server plus Claude skills for the OpenAI Ads Advertiser API (ChatGPT Ads). Python, uv, Docker.

Built because the existing servers miss three things that cost money in practice: they do not retry failed requests, they do not bound their responses, and they do not know the quirks of the location dataset.

What this does differently

Retries on 409. The API sporadically answers 409 "Ad Account is missing details." with no relation to the request. In a probe on 2026-09-04 that hit 8 of 32 calls; with backoff all 32 succeeded. Other servers surface this as a failure when nothing is wrong.

Response budgeting. Insights return up to 2000 rows. Serialised verbatim that fills the context window before the model can evaluate anything. This server trims the longest lists and records what it trimmed and how to fetch the rest.

Location lookup that works. Three pitfalls, all verified live:

Input	Results	Reason
Muenchen	0	umlauts are mandatory
München	78
Munich	0 with country_code: DE	exonyms do not resolve
a German Landkreis	0	districts are not a location type

The server rewrites ASCII transliterations, maps English exonyms, and says so when a query names something that is not a location type.

Postcode targeting. The documentation lists country, region and dma. There is a fourth type:

{"id": "10015849", "type": "postal_code",
 "canonical_name": "10115, Berlin, Germany", "country_code": "DE"}

Conversely, dma never appeared for Germany in any query.

Micros with an explanation. Two expensive traps:

• CPM: max_bid_micros is the price per single impression, not per 1000. A 60.00 CPM is 60000, not 60000000.
• oCPC: max_bid_micros is a CPA bid, yet billing happens per click.

explain_micros converts a value and states what it means.

Safety

• Every create tool forces status="paused" at the type level. A newly created campaign cannot spend until it is explicitly activated.
• Budgets above the ceiling require confirm_budget=True.
• archive is irreversible and requires confirm_archive=True.
• OPENAI_ADS_MCP_READONLY=1 registers no write tools at all.
• The API key is never logged and never appears in error messages.

Install

uv sync --group dev
uv run pytest

PyPI: chatgpt-ads-mcp

Configuration

Copy .env.example to .env:

OPENAI_ADS_API_KEY=sk-...      # from https://ads.openai.com → Settings
OPENAI_ADS_BUDGET_CEILING=500  # confirmation threshold
OPENAI_ADS_CURRENCY=EUR
OPENAI_ADS_MCP_READONLY=0

One key covers exactly one ad account; there is no account_id parameter.

Add it to Claude Code

One command, no clone and no install — uvx fetches the package on first run:

claude mcp add chatgpt-ads -e OPENAI_ADS_API_KEY=sk-... -- uvx chatgpt-ads-mcp

Read-only, if you want to look before you touch anything:

claude mcp add chatgpt-ads \
  -e OPENAI_ADS_API_KEY=sk-... \
  -e OPENAI_ADS_MCP_READONLY=1 \
  -- uvx chatgpt-ads-mcp

Verify with claude mcp list, then ask the assistant to call get_account.

Other MCP clients

Any client that speaks stdio takes the same command:

{
  "mcpServers": {
    "chatgpt-ads": {
      "command": "uvx",
      "args": ["chatgpt-ads-mcp"],
      "env": { "OPENAI_ADS_API_KEY": "sk-..." }
    }
  }
}

To run from a checkout instead, use "command": "uv" with "args": ["run", "--directory", "/path/to/openai-ads-tools", "chatgpt-ads-mcp"].

Docker

docker build -t openai-ads-mcp .
docker run --rm -i --env-file .env openai-ads-mcp

Tools

Read: get_account, list_accounts, check_feature_access, search_geo, list_campaigns, get_campaign, list_ad_groups, get_ad_group, list_ads, get_ad, get_insights, list_audiences, list_conversion_setup, explain_micros

Write: create_campaign, create_ad_group, create_ad, upload_creative, set_account_brand, preview_ad, set_campaign_state, set_ad_group_state, set_ad_state, set_negative_keywords

check_feature_access is the quickest way in: it reports which features the account has. This API answers 403, and sometimes 404, when a feature is gated — not only when something is missing.

Skills

skills/ holds Claude skills covering the judgement the API does not:

Skill	Purpose
ads	Orchestrator, shared reference files
ads-campaign	Clarify, decide, create
ads-creative	Copy, character economy, validation
ads-hints	Topic clusters, negative keywords
ads-geo	Postcode sets instead of a radius
ads-audit	Reporting with a data sufficiency gate

The three reference files under skills/ads/references/ are the substance: platform-facts.md (only what is sourced), misinformation.md (what is falsely claimed, with corrections), benchmarks.md (sufficiency thresholds and how to derive a tolerable CPA).

Copy validator

uv run python scripts/validate_creative.py \
  --title "Prototype in 4 weeks" --body "..." --brand "YourBrand"

Counts grapheme clusters rather than code points, and checks the rules language models actually break: dashes, rules of three, filler verbs, invisible characters. The style rules target German copy, which is the harder case; the length and structural checks are language-independent.

What the platform cannot do

So nobody goes looking:

• No keyword targeting, no match types, no keyword planner. Delivery is steered through context_hints — free text, evaluated semantically.
• No reporting per context hint. The ad group is the smallest unit of measurement. Put 2000 hints in one group and you will never learn which worked. Keep hints few per group and use more groups.
• No radius targeting. Model a catchment area as a list of postcodes.
• No personalised ads in the EEA or Switzerland.
• No published benchmarks. There is no official average CTR or reference CPC. Figures circulating as such are unsourced.
• No sandbox. Create everything paused and use preview_ad.

Character limits per OpenAPI spec v2.3.0: title 3–50, body at most 100. The 35 and 67 figures in circulation contradict the spec.

Status

Early. The read path and the safety model are exercised against a live account; the write path is implemented and unit-tested but has seen limited production use. Treat create_* as beta and check the preview before activating anything.

License

MIT. See NOTICE for patterns adopted from other projects.