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Google_ads

by io.github.pipeworx-io

Gads List Ad Groups

gads_list_ad_groups
Read-onlyIdempotent

List ad groups within a campaign by campaign ID. Returns ad group names, IDs, statuses, and CPC bids. Use to explore campaign structure or select an ad group for analysis.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of ad groups to return (default 50)
statusNoFilter by ad group status (optional)
campaign_idYesCampaign ID to list ad groups for
customer_idYesGoogle Ads customer ID

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultsNoList of ad groups in campaign

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "results": {
+      "description": "List of ad groups in campaign",
+      "items": {
+        "properties": {
+          "ad_group": {
+            "description": "Ad group resource data",
+            "properties": {
+              "cpc_bid_micros": {
+                "description": "CPC bid in micros",
+                "type": "number"
+              },
+              "id": {
+                "description": "Ad group ID",
+                "type": "string"
+              },
+              "name": {
+                "description": "Ad group name",
+                "type": "string"
+              },
+              "status": {
+                "description": "Ad group status (ENABLED, PAUSED, REMOVED)",
+                "type": "string"
+              },
+              "type": {
+                "description": "Ad group type",
+                "type": "string"
+              }
+            },
+            "type": "object"
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "campaign_id": "9876543210",
+    "customer_id": "1234567890"
+  },
+  {
+    "campaign_id": "9876543210",
+    "customer_id": "1234567890",
+    "limit": 30,
+    "status": "ENABLED"
+  }
+]
  3. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint, providing safety context. The description adds value by specifying what data is returned (ad group names, IDs, statuses, CPC bids) and the intended use cases, which helps the agent understand the behavior beyond the annotations.

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 two sentences, front-loading the action and quickly covering what is returned and when to use it. Every sentence contributes value without redundancy.

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?

Given the tool's simplicity (4 parameters, read-only, output schema exists), the description covers the essential aspects: purpose, key parameters, returned fields, and use cases. It does not discuss pagination or default limit details, but that is a minor gap for a listing operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the input schema already describes all four parameters well. The description mentions filtering by campaign ID (campaign_id) but does not add significant extra meaning beyond the schema. It does not clarify the limit default or status options, which are already in the schema. Baseline 3 is appropriate.

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 clearly states the action (list ad groups), the resource (within a campaign), and the unique identifier (campaign ID). It lists returned fields (names, IDs, statuses, CPC bids) and provides a use case (explore campaign structure or select an ad group), distinguishing it from sibling tools like gads_list_campaigns.

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?

The description includes a usage statement ('Use to explore campaign structure or select an ad group for analysis') which implies when to use, but does not explicitly mention when not to use or provide direct comparisons to alternative tools. The context of sibling tools helps, but the description lacks explicit guidance.

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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TDQS

A3.7/5.0
Disambiguation2/5

The five gads_* tools are distinct, but the majority of the surface is a sprawling research/meta toolkit with many overlapping retrieval entry points: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, suggest_questions, validate_claim, entity_profile, compare_entities, recent_changes, and search_within all cover overlapping information-query territory. An agent could easily misroute a question among the ask_pipeworx variants or between the general-query and company-profile tools.

Naming Consistency3/5

Domain prefixes like gads_, polymarket_, and ask_pipeworx_ provide some structure, but naming conventions are mixed: gads_list_campaigns and list_subscriptions follow verb_noun, while entity_profile, ai_visibility_check, remember, and generate_llms_txt do not. The names are readable and grouped by prefix, but they do not form one consistent pattern.

Tool Count2/5

At 36 tools this is a large surface, and the count becomes even more problematic because the server is named Google_ads while only 5 of the 36 tools relate to Google Ads. The other 31 tools are a broad Pipeworx data-research, prediction-market, memory, and subscription utility set, which makes the server feel bloated and mis-scoped for its advertised purpose.

Completeness2/5

As a Google Ads server, the surface is read-only and incomplete: it can list campaigns and ad groups, get campaign details, pull metrics, and run GAQL, but it cannot create, update, or delete campaigns, manage budgets and bids, or handle keywords, audiences, or ad creatives. The many unrelated data-research tools do not address these core Google Ads management gaps.

Resources