Google_ads

Gads Search

gads_search

Read-onlyIdempotent

Run custom GAQL queries against Google Ads data. Use for advanced analysis—filter by keywords, matching types, or aggregate metrics by custom dimensions.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`query`	Yes	GAQL query string (e.g., "SELECT campaign.name, metrics.clicks FROM campaign WHERE segments.date DURING LAST_7_DAYS")
`customer_id`	Yes	Google Ads customer ID

Output Schema

TableJSON Schema

Name	Required	Description	Default
`results`	No	GAQL query results

Schema Changelog

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

Changed1 schema field changedMay 8, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "results": {
+      "description": "GAQL query results",
+      "items": {
+        "description": "Result row matching the GAQL query selection",
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "type": "object"
+}

Changed1 schema field changedMay 6, 2026

addedInput schema / examples

Added value: +[
+  {
+    "customer_id": "1234567890",
+    "query": "SELECT campaign.name, metrics.clicks FROM campaign WHERE segments.date DURING LAST_7_DAYS"
+  },
+  {
+    "customer_id": "1234567890",
+    "query": "SELECT ad_group.name, metrics.impressions, metrics.cost_micros FROM ad_group WHERE campaign.id = 9876543210"
+  }
+]

First observedApr 22, 2026

TDQS

A4/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, so the description need not repeat these. The description adds no behavioral traits beyond annotations, but does not contradict them. A score of 3 is appropriate as the description adds marginal behavioral context.

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: one for the core action, one for usage context. It is front-loaded, concise, and contains no redundant information. Every sentence adds value.

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 presence of an output schema and comprehensive annotations, the description adequately covers usage context. It could mention return format or limitations, but completeness is high for a tool with strong structured metadata.

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 both parameters documented. The description adds examples of filtering and aggregation, providing context beyond schema but not essential new semantics. Baseline 3 is justified as the schema does the primary work.

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 tool runs custom GAQL queries against Google Ads data, using specific verbs and resource. It distinguishes from sibling tools like gads_campaign_metrics by emphasizing advanced analysis and custom dimensions, making the purpose unambiguous.

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 provides clear context for usage using 'Use for advanced analysis' and examples, but does not explicitly state when not to use this tool versus alternatives like gads_campaign_metrics or gads_list_campaigns. The guidance is adequate but lacks exclusions.

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.

Gads Search

Input Schema

Output Schema

Schema Changelog

TDQS

Glama MCP Gateway

TDQS

Resources