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shopify-chatgpt-ads-mcp

shopify-chatgpt-ads-mcp

by Avada-AI
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Local

Campaigns as Avada sees them

measurement_list_campaigns
Read-only

List ad campaigns with status, budget, and health; enable include_issues to surface ad groups, ads, and serving issues for diagnosing delivery problems.

Instructions

Campaigns with status, budget and health; include_issues adds ad groups, ads and serving issues (slow). 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_list_campaigns"
  }
}

Input Schema

NameRequiredDescriptionDefault
include_issuesNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4/5.0
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 value beyond that: preview API surface, required AVADA_API_KEY, the cost/latency of include_issues, and an explicit no-fabrication rule on error responses.

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 compact sentences, front-loaded with the payload and followed by prerequisites and failure handling. No filler; the only friction is the dense parenthetical stacking of API/preview/key details.

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 output schema and no annotations describing return shape, the description still names the returned fields and the expanded payload, and covers prerequisites and error behavior. Pagination/result limits are unmentioned, which is the main remaining gap for a list tool.

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?

Schema coverage is 0% (a bare boolean with no description), so the description must carry the parameter. It does: include_issues is explained in terms of what it expands (ad groups, ads, serving issues) and its slowness cost. That is meaningful compensation, though it could say what the default is when omitted.

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 resource (campaigns) and the fields returned (status, budget, health), plus the differentiating 'as Avada sees them' / Avada preview API framing. It does not explicitly distinguish itself from the sibling list_campaigns, which would be the obvious confusion point, so it stops short of a 5.

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?

Gives clear context on when the include_issues flag is worth paying for ('adds ad groups, ads and serving issues (slow)'') and what to do when the API responds unavailable. It offers no exclusion or comparison against list_campaigns, so the alternative-selection guidance is incomplete.

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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