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Python
Hybrid

meta_ads_excluded_placements_set

Replace a Meta ad set's excluded publisher lists, categories, and brand-safety content types while preserving other targeting. Returns applied facets and Meta's update result.

Instructions

Sets one ad set's delivery-surface exclusions. Returns ad_set_id, the applied facets, and Meta's update result. Mutating and delivery-affecting — excluding too much can take an ad set to zero delivery. Each supplied facet REPLACES its current value (Meta has no append here); an omitted facet is left untouched. The rest of the targeting spec (geo, audiences, interests) is preserved by a read-modify-write merge. Recorded in STATE.json's action_log with an observation window and reversible via rollback_apply, which restores the prior lists.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
reasonNoWhy this change is being made: one or two sentences naming the evidence and the expected effect. Stored in the journal and on the action_log entry this call produces, for the operator and the next session.
ad_set_idYesAd set ID whose exclusions to read or write (e.g. '23851234567890123'). Exclusions live on the ad set, not on the campaign — set them per ad set.
account_idNoAd account ID in 'act_XXXXXXXXXX' form. Optional — falls back to META_ADS_ACCOUNT_ID from the configured credentials.
excluded_publisher_list_idsNoComplete new value for targeting.excluded_publisher_list_ids — numeric ids of Audience Network publisher block lists to apply. Replaces the current list; an empty array clears the facet.
excluded_publisher_categoriesNoComplete new value for targeting.excluded_publisher_categories — Audience Network publisher/app categories to exclude. Replaces the current list rather than appending, so read the current value with meta_ads_excluded_placements_get first and send the full intended set. An empty array clears the facet.
excluded_brand_safety_content_typesNoComplete new value for targeting.excluded_brand_safety_content_types — content types to exclude. Replaces the current list; an empty array clears the facet.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.20.0
    • addedInput schema / properties / reason
      Added value: +{
+  "description": "Why this change is being made: one or two sentences naming the evidence and the expected effect. Stored in the journal and on the action_log entry this call produces, for the operator and the next session.",
+  "maxLength": 500,
+  "type": "string"
+}
  2. Addedv0.10.44

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description carries full behavioral burden and does so richly: it flags mutation and delivery risk, explains replace semantics, says omitted facets are untouched, and clarifies that other targeting is preserved via a read-modify-write merge. It also discloses STATE.json action_log recording and rollback_apply reversibility.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact yet information-dense, front-loading the core action and return value before the risk, merge semantics, and rollback behavior. Every sentence adds necessary operational or safety context with no repetition.

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?

Despite having no annotations and no output schema, the description covers what an agent needs for safe invocation: return shape, mutation risk, facet replacement behavior, preservation of other targeting, audit logging, and reversibility. The schema handles the remaining parameter-level details.

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% and each parameter is already well documented, so the baseline is 3. The description adds meaningful cross-cutting semantics: each supplied facet replaces its current value, an omitted facet is left untouched, and the rest of the targeting spec is preserved. This clarifies how the array parameters behave as a group.

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?

Description names a specific verb ('Sets') and resource ('one ad set's delivery-surface exclusions'), immediately distinguishing it from read-style siblings like meta_ads_excluded_placements_get. It also explains the operation's scope and return value, so an agent knows exactly what this tool does.

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?

Clear context is provided: the tool replaces delivery-surface exclusion facets on a single ad set, and the warning about zero delivery signals caution. The replace-not-append and omitted-facet-untouched semantics imply that reading current values first is necessary, though the description does not explicitly name the alternative get tool or state when-not-to-use.

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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