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Google Ads MCP Server

Google Ads MCP Server

execute_gaql

Read-only

Run arbitrary Google Ads Query Language (GAQL) read queries to retrieve account data not covered by other tools.

Instructions

Execute an arbitrary GAQL (Google Ads Query Language) query.

This is the most flexible tool -- use it for any read query not covered by other tools.
GAQL reference: https://developers.google.com/google-ads/api/docs/query/overview

Args:
    customer_id: Google Ads customer ID.
    query: Full GAQL query string (SELECT ... FROM ... WHERE ...).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "customer_id": "<customer_id>",
      "query": "<query>"
    },
    "name": "execute_gaql"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYes
customer_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds that the query is arbitrary and raw (implying no validation layer) and links the external GAQL reference, but says nothing about pagination, row limits, malformed-query behavior, or what the result set looks like.

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

Conciseness4/5

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

Two short front-loaded sentences plus a compact Args block; no filler. The doc-link line and args header add a little structural overhead but each line still carries information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-parameter arbitrary-query tool with no output schema, the definition covers inputs adequately but leaves the return shape and result-size behavior entirely implicit. An agent knows how to call it but not what to expect back or how failures surface.

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 description coverage is 0%, so the description must carry the load. It does define both args and sketches query syntax as 'SELECT ... FROM ... WHERE ...', which is genuinely useful, but it never specifies the customer ID format (digits vs dashes) or whether LIMIT/paging clauses are honored.

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?

States a specific verb and resource (execute an arbitrary GAQL query) and immediately positions itself against the rest of the toolset: it is the flexible read path for anything the specialized getters don't cover. An agent can distinguish it from list_gaql_resources or get_campaign without opening a schema.

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?

Explicitly routes usage: 'use it for any read query not covered by other tools,' which functions as both a when-to-use and an implicit when-to-prefer-alternatives rule. It stops short of naming a concrete sibling or warning about the cost/error profile of hand-written queries, so it is clear but not exhaustive.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.