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Google_ads

by io.github.pipeworx-io

Gads List Campaigns

gads_list_campaigns
Read-onlyIdempotent

List all campaigns in your Google Ads account. Returns campaign names, IDs, statuses, budgets, and types. Use to overview account structure or find a campaign ID for detailed analysis.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of campaigns to return (default 50)
statusNoFilter by campaign status (optional, returns all if omitted)
customer_idYesGoogle Ads customer ID (e.g., "1234567890" or "123-456-7890")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultsNoList of campaigns from GAQL search results

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 campaigns from GAQL search results",
+      "items": {
+        "properties": {
+          "campaign": {
+            "description": "Campaign resource data",
+            "properties": {
+              "advertising_channel_type": {
+                "description": "Advertising channel type",
+                "type": "string"
+              },
+              "bidding_strategy_type": {
+                "description": "Bidding strategy type",
+                "type": "string"
+              },
+              "id": {
+                "description": "Campaign ID",
+                "type": "string"
+              },
+              "name": {
+                "description": "Campaign name",
+                "type": "string"
+              },
+              "status": {
+                "description": "Campaign status (ENABLED, PAUSED, REMOVED)",
+                "type": "string"
+              }
+            },
+            "type": "object"
+          },
+          "campaign_budget": {
+            "description": "Campaign budget information",
+            "properties": {
+              "amount_micros": {
+                "description": "Budget amount in micros",
+                "type": "number"
+              }
+            },
+            "type": "object"
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "customer_id": "1234567890"
+  },
+  {
+    "customer_id": "123-456-7890",
+    "limit": 25,
+    "status": "ENABLED"
+  }
+]
  3. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description is not burdened with safety disclosure. It adds value by specifying the fields returned (names, IDs, statuses, budgets, types). No contradictions with annotations. However, it could mention pagination or performance implications for large accounts.

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?

Two sentences, front-loaded with the action verb 'List'. Every sentence adds value: first states purpose and return data, second gives usage guidance. No wasted words.

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?

Given the presence of an output schema and robust annotations, the description is complete. It covers the tool's purpose, return values, and a common use case. The parameter count (3, 1 required) is moderate and well-addressed by the schema.

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% with descriptions for all three parameters. The description does not add meaning beyond the schema (e.g., does not explain limit default behavior or customer_id format). Baseline 3 is appropriate as the schema already provides the necessary semantics.

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 'List all campaigns' with a specific verb and resource. It lists the return fields (names, IDs, statuses, budgets, types) and provides a use case: overview account structure or find a campaign ID. This effectively distinguishes it from sibling tools like gads_campaign_metrics or gads_get_campaign.

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?

The description gives explicit usage context: 'Use to overview account structure or find a campaign ID for detailed analysis.' While it does not mention when not to use or directly compare to siblings, the guidance is clear and actionable. The context signals and sibling list further aid differentiation.

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