feat(demographics): add age/gender/parental/income targeting tools - #38
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chrmoller
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May 22, 2026
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- get_demographic_targeting (read): list existing demographic criteria on an ad group or campaign, with a composite remove_id ready for remove_entity
- draft_demographic_targeting (write): propose exclusion (default) or positive demographic criteria. Accepts canonical enums and human-readable aliases (e.g. "25-34", "female", "top-10"). Surfaces warnings for near-total-dimension exclusions and positive narrowing.
- _apply_add_demographic_criteria: applies AGE_RANGE / GENDER / PARENTAL_STATUS / INCOME_RANGE criteria at ad-group or campaign level
- remove_entity: accepts ad_group_criterion and campaign_criterion as semantic aliases so removals read sensibly in plans and audit logs
- Orchestration rules document the bucket mismatch case (e.g. "23-35" has no exact mapping) and the audience-vs-demographic distinction
- get_demographic_targeting (read): list existing demographic criteria on an ad group or campaign, with a composite remove_id ready for remove_entity - draft_demographic_targeting (write): propose exclusion (default) or positive demographic criteria. Accepts canonical enums and human-readable aliases (e.g. "25-34", "female", "top-10"). Surfaces warnings for near-total-dimension exclusions and positive narrowing. - _apply_add_demographic_criteria: applies AGE_RANGE / GENDER / PARENTAL_STATUS / INCOME_RANGE criteria at ad-group or campaign level - remove_entity: accepts ad_group_criterion and campaign_criterion as semantic aliases so removals read sensibly in plans and audit logs - Orchestration rules document the bucket mismatch case (e.g. "23-35" has no exact mapping) and the audience-vs-demographic distinction Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The draft_demographic_targeting tool used raw list[str] | None for its four demographic-list parameters, which skipped the project's JSON-string-to-list coercion (the _StrListOpt validator introduced in kLOsk#28). MCP clients that pre-serialize list params as JSON strings — e.g. age_ranges="[\"25-34\"]" — hit Pydantic's list_type error. Switch all four to _StrListOpt to match the convention used by every other list-accepting tool in server.py (geo_target_ids, language_ids, keywords, callouts, image_paths, campaign_ids, etc.). Bare-scalar inputs like "65+" still fail by design — clients must send a real array or a JSON-encoded list. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Same root cause as the draft_demographic_targeting fix (642d870): the headlines and descriptions params used raw list[str | dict] instead of going through the project's JSON-string-to-list coercion validator. Add a new _StrOrDictList alias (no existing one fits because the RSA params accept mixed string-or-dict entries for unpinned vs pinned assets) and apply it. Add a regression test that submits JSON-encoded list strings — the same failure mode users would hit with pre-serializing MCP clients. Audit complete: every other list-accepting MCP tool in server.py already uses _StrList/_StrListOpt/_DictList/_DictListOpt, so this is the last remaining instance. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The four demographic list params (age_ranges, genders,
parental_statuses, income_ranges) were declared as
`_StrListOpt = None`, which Pydantic encodes as
`{"anyOf": [{"type": "array"}, {"type": "null"}], "default": null}`.
That's spec-correct but some MCP clients only inspect the top-level
`type` field and never traverse `anyOf`, so they see no `type: array`
and refuse to serialize a list. Practical symptom: clients send the
list as a bare string and the Pydantic validator rejects it.
Switch to `_StrList = []` (with the same noqa: B006 comment used by
discover_keywords' seed_keywords). The resulting schema is flat:
`{"type": "array", "items": {...}, "default": []}` — every client
sees `type: array` and serializes correctly. The "at least one
dimension must be non-empty" validation already works with `[]` as
the empty sentinel (no change needed).
This is a targeted fix; the same pattern exists on other tools
(`get_recommendations.recommendation_types`,
`attribution_check.conversion_events`, etc.) and would benefit
from the same cleanup — left for a follow-up.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Owner
|
This is exactly how contributions to this repo should look: full preview/confirm compliance, real tests, and both rules files updated. Three asks before merge: (1) rebase onto v0.10.0, the module-level |
kLOsk
added a commit
that referenced
this pull request
Jul 10, 2026
PR #38 predated the multi-tenant runtime refactor; both new tools referenced the removed module-level _config global (same class of bug as 2bdd92e — caught by the static regression guard this time). Also drop the duplicate _FakeCampaignCriterionService the merge brought in. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01EvgwTbTqEinTJXNriN5ga1
Owner
|
Merged to main — thank you @chrmoller! This is the reference-quality contribution for this repo: full preview/confirm compliance, real MCP-schema-level tests (a pattern we'll reuse), expert rules-file guidance, and it even fixed main's docs-vs-code gap on Rather than another review round-trip, I applied the three asks from my last comment maintainer-side during the merge, plus one extra guard:
Ships in the next release as tools #48–49. Hope to see more from you! |
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