Google Ads MCP Server

get_conversion_attribution

Analyze Google Ads conversion attribution by supplying customer ID and optional conversion action ID, revealing which campaigns and keywords drive conversions.

Instructions

Get conversion attribution analysis

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": {
      "customerId": "<customerId>"
    },
    "name": "get_conversion_attribution"
  }
}

Input Schema

Name	Required	Description	Default
`customerId`	Yes	Google Ads customer ID
`conversionId`	No	Optional conversion action ID

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 9, 2026

TDQS

C2/5.0

Behavior1/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not mention what data is returned, whether any filters or date ranges apply, access requirements, or any side effects. This leaves the tool's behavior completely opaque.

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

Conciseness2/5

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

The description is a single short sentence, which is concise, but it is under-specified and does not earn its place as it adds almost no information beyond the tool name. For a tool of this complexity, a minimal one-liner is not satisfying.

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

Completeness2/5

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

Given that there is no output schema and no annotations, the description should explain what the analysis provides and how to interpret results, but it does not. The parameters are documented in the schema, but the description fails to provide any context about the tool's output or usage, leaving significant gaps.

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 100% for the two parameters (customerId and conversionId), so the schema already explains their meaning. The description adds no additional parameter semantics, which is acceptable given the high coverage baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Get conversion attribution analysis' provides a clear verb ('Get') and a general resource ('conversion attribution analysis'), but the object is vague and does not distinguish it from sibling tools like get_conversion_path_data or get_conversions, which also deal with attribution metrics. The resource could be more specific.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this tool versus alternatives. No context is given about scenarios, prerequisites, or why a user would select this instead of related tools like get_conversion_path_data or get_conversions.

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

Deploy Server