paid-media-mcp

get_roas_comparison

Compare platform-reported ROAS, MTA attributed ROAS, and margin ROI for each channel. Quantify platform attribution inflation to justify budget reallocation.

Instructions

Compare platform-reported ROAS vs MTA attributed ROAS vs margin ROI for each channel. Returns platform_overcount_pct — the percentage by which platforms over-claim credit compared to the attribution model. Use this to quantify platform attribution inflation and justify budget reallocation decisions. Requires BigQuery mode.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_roas_comparison"
  }
}

Input Schema

Name	Required	Description	Default
`channel`	No
`platform`	No
`conversion_type`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.0.0Jul 28, 2026

TDQS

A3.5/5.0

Behavior3/5

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

No annotations provided, so description carries full burden. It discloses the BigQuery prerequisite and return metric, but does not declare read-only nature or other behavioral traits.

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 efficiently convey purpose, output, use case, and requirement. No wasted words.

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 3 undocumented parameters and no output schema, the description lacks details on parameter semantics and return structure. Incomplete for a tool with these gaps.

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

Parameters1/5

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

Schema has 3 string parameters with 0% description coverage. The description does not explain their meaning, accepted values, or constraints. Fails to compensate for lack of schema descriptions.

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 it compares three ROAS metrics per channel, returns platform_overcount_pct, and specifies its use case. It is distinct from sibling tools like get_attribution_results or get_channel_efficiency.

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 says to use for quantifying attribution inflation and budget decisions, and notes BigQuery mode requirement. Does not mention when not to use or list alternatives.

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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