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shopify-chatgpt-ads-mcp

shopify-chatgpt-ads-mcp

by Avada-AI

Feed status (Avada)

measurement_get_feed_status
Read-only

Check the sync status of a Shopify product feed to OpenAI: retrieve the last snapshot, delta updates, and quality score to verify feed health and confirm data is current.

Instructions

The app's product feed sync to OpenAI: last snapshot, delta updates, quality score. 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_feed_status"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint, and the description adds genuinely new context: it is a preview API, it requires AVADA_API_KEY, and on an unavailable result the agent must report the reason and steps rather than fabricate or zero-fill numbers. That anti-hallucination guardrail is the kind of behavioral detail annotations cannot convey.

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 lead with what the tool reports, then the source and auth requirement, then the failure-mode instruction. Nothing is redundant, though the phrase 'The app's product feed sync to OpenAI:' is slightly elliptical about whether snapshot/delta/quality are returned fields or feed properties.

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?

For a zero-argument, read-only status tool with no output schema, the description covers the key return concepts, the auth prerequisite, and the degraded-result path. The absence of an output schema means return shape is not fully pinned down, but the essentials an agent needs to invoke and interpret it are present.

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?

There are zero parameters, so the baseline is 4 and there is no parameter meaning to add or omit. The description spends its words on output content and failure behavior instead, which is the right allocation.

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?

The description names a specific resource and payload: the product feed sync's last snapshot, delta updates, and quality score. That is far more informative than the title alone, though it does not explicitly distinguish this from nearby siblings such as measurement_get_setup_status or measurement_list_feed_issues.

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

Usage Guidelines3/5

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

Usage is only implied — an agent can infer this is the tool to check feed sync health — and no alternative or exclusion is named. The 'if it answers unavailable' clause gives error-handling guidance rather than selection guidance, so it does not fully satisfy the when-to-use bar.

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