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shopify-chatgpt-ads-mcp

shopify-chatgpt-ads-mcp

by Avada-AI
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Local

Spend pacing

measurement_get_spend_pacing
Read-only

Measure month-to-date ad spend against the target and forecast pacing; if unavailable, explain the reason and steps instead of zero-filling numbers.

Instructions

Month-to-date spend against the target set in the app, with a forecast. From Avada ChatGPT Ads (preview API, needs AVADA_API_KEY). If it answers unavailable, tell the user the reason and steps; never invent or zero-fill numbers.

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": "measurement_get_spend_pacing"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=true, so the safety profile is covered. The description adds real context beyond that: it is a preview API requiring an AVADA_API_KEY, it can return an 'unavailable' response, and the agent must report the reason rather than fabricate or zero-fill values.

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?

Three short sentences that front-load what is returned, then the data source and auth requirement, then the failure-handling rule. No filler, though the 'preview API' note could be tightened.

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?

With no parameters and no output schema, the description carries the burden of describing the return: MTD spend, target, and forecast are all named. The API-key prerequisite and the 'unavailable' failure mode are covered, which is what an agent needs to call and interpret it.

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

Parameters4/5

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

The tool takes zero parameters and the schema is an empty object, so there is nothing for the description to disambiguate. Baseline 4 applies.

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

Purpose4/5

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

States a specific metric and scope: month-to-date spend against an in-app target, plus a forecast. The verb is implicit ('retrieves') rather than explicit, and no sibling tool is named for contrast, but the returned content is unambiguous enough that an agent knows what it gets.

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

Usage Guidelines2/5

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

No statement of when to call this instead of measurement_get_account_overview, measurement_get_ads_report, or the other measurement_* siblings. The only conditional guidance is about failure handling ('if it answers unavailable, tell the user the reason'), which is not selection guidance.

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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