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ListingGood MCP

by io.github.ryanyang828

agent_ready_check

Score whether a product listing is Agent-Ready for AI shopping agents (ACP/UCP era).

Platform-agnostic: works for Amazon, Shopify, Walmart, TikTok Shop or any storefront that
AI shopping assistants may read. Use it when a user asks whether their product will be
found, recommended, or auto-purchased by an AI agent.

Fully local, deterministic rule engine — no API key, no credits, no network call. Returns
four dimensions: structured attributes, entity clarity, trust & compliance, and agent
actionability, plus ranked fixes.

Args:
    text: raw product copy — title plus bullets/description (required).
    platform: amazon | shopify | walmart | tiktok | generic | auto (default auto-detected).
    lang: en or zh for the report language (default en).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoen
textYes
platformNoauto

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations present, the description carries the full behavioral burden and delivers strongly: 'Fully local, deterministic rule engine — no API key, no credits, no network call' clearly communicates side-effect-free local execution. It also enumerates the returned dimensions plus ranked fixes, giving agents a solid understanding of what the tool will produce.

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?

The description is information-dense with no filler. Every sentence earns its place: purpose, platform scope, use-case trigger, execution model, output summary, and a compact Args block. The front-loaded purpose statement makes it immediately scannable.

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 tool with no output schema and no annotations, the description provides a solid high-level contract: required inputs, supported options, execution behavior, and the four output dimensions. It stops short of specifying the score scale or exact return structure, which an agent may need when interpreting results, but it is sufficient for invoking the tool correctly.

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

Parameters5/5

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

Schema description coverage is 0%, but the description compensates by defining all three parameters: text is raw product copy and is required; platform lists accepted values including auto-detection; lang specifies report language options with defaults. Each parameter's meaning is clear without needing to infer from the schema.

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 opens with a specific verb and resource: 'Score whether a product listing is Agent-Ready for AI shopping agents.' It also states the platform-agnostic scope clearly, covering Amazon, Shopify, Walmart, TikTok Shop, and other storefronts. However, it does not explicitly differentiate itself from the similarly named sibling ai_readiness_check, leaving some potential overlap ambiguity.

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?

The description gives an explicit trigger: 'Use it when a user asks whether their product will be found, recommended, or auto-purchased by an AI agent.' This is a concrete, operational use case. It does not, however, mention when not to use this tool or point to alternatives among the sibling tools.

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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TDQS

A4.4/5.0
Disambiguation4/5

Most tools have clearly distinct roles: generation, compliance, review analysis, and POA drafting are separated. The two readiness checks (agent_ready_check vs ai_readiness_check) and the two compliance tools (compliance_check vs compliance_scan) could be confused by name alone, but their descriptions explicitly clarify scope and intended use.

Naming Consistency3/5

Four tools consistently use verb_noun naming (analyze_review, fill_from_sentence, generate_listing, generate_poa), while the other four use compound noun phrases (agent_ready_check, ai_readiness_check, compliance_check, compliance_scan). All names are snake_case and readable, but the mixed verb/noun convention is noticeable and not fully consistent.

Tool Count5/5

Eight tools is well within the ideal 3-15 range and each tool maps to a distinct step in the listing lifecycle: input expansion, listing generation, compliance pre-check, deep compliance audit, AI/agent readiness scoring, review response, and appeal drafting. No tool feels redundant or out of place.

Completeness4/5

The toolset covers the main listing workflow well: expanding rough input, generating optimized listings, checking compliance, auditing deeply, scoring AI readiness, handling reviews, and drafting POAs. A minor gap is the lack of a dedicated tool for editing or optimizing an already-existing listing, but the core lifecycle is otherwise well covered.

Resources