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ListingGood MCP

by io.github.ryanyang828

ai_readiness_check

Score how likely Amazon's AI (Rufus, COSMO) is to recommend a listing.

Free, deterministic, rule-based check on pasted listing copy (title, bullets, description).
Returns a compliance health score, an AI-readability score, and a combined AI Recommendation
Readiness Score (compliance * 0.55 + readability * 0.45) with actionable suggestions.

Use this as a fast baseline BEFORE generating or editing a listing. Do NOT use it for a full
compliance report - use compliance_scan for the deep knowledge-base audit. Free, read-only,
no API key required, no credits deducted.

Args:
    text: raw listing title + bullets + description (required).
    marketplace: marketplace code, US/DE/ES/FR/IT/JP/AE/SA/UK (default US).
    lang: zh or en (default en).
    email: optional lead email for a confirmation message and lead capture.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoen
textYes
emailNo
marketplaceNoUS

TDQS

A5/5.0
Behavior5/5

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

Annotations are empty, so the description fully carries the burden of behavioral disclosure. It states the tool is free, deterministic, rule-based, read-only, requires no API key, and deducts no credits. It also discloses the exact scoring formula and that it returns actionable suggestions, giving agents a clear model of what invoking it will do.

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 front-loaded with the core purpose and deliverables, then moves to usage guidance and then parameters. Every sentence adds value: the formula, the exclusion of compliance_scan, the cost/access characteristics, and the parameter meanings are all necessary for correct invocation.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and no annotations, the description provides everything needed to call the tool correctly: purpose, input format, parameter domains, default values, output composition, and when not to use it. It even explains why email might be supplied, covering the lead-capture side effect clearly.

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%, so the description must compensate for the schema's silence. It does this thoroughly: text is defined as 'raw listing title + bullets + description,' marketplace lists valid codes, lang specifies zh/en, and email is described as an optional lead-capture field. This exceeds what the bare schema provides.

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 opens with a specific verb and resource: 'Score how likely Amazon's AI (Rufus, COSMO) is to recommend a listing.' It clearly distinguishes itself from sibling compliance_scan by labeling itself a fast baseline check, so an agent can tell which tool to use.

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

Usage Guidelines5/5

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

It gives explicit timing: 'Use this as a fast baseline BEFORE generating or editing a listing.' It also names the alternative for deeper audits: 'Do NOT use it for a full compliance report - use compliance_scan for the deep knowledge-base audit.' This leaves no ambiguity about when to choose this tool.

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

A4.3/5.0
Disambiguation4/5

Most tools map to clearly distinct workflow stages: sentence-to-listing, listing generation, compliance, review analysis, and POA drafting. The main ambiguity is between ai_readiness_check and compliance_check, which both offer fast pre-listing checks with some compliance overlap, though the descriptions try to separate them by purpose.

Naming Consistency3/5

All names are snake_case and readable, but the naming convention is not uniform: analyze_review, fill_from_sentence, generate_listing, and generate_poa are verb-first, while ai_readiness_check, compliance_check, and compliance_scan are noun-first check/scan labels. This mixed pattern is understandable but not predictable.

Tool Count5/5

Seven tools is a well-scoped set for an Amazon listing content and compliance assistant. Each tool has a distinct role in the workflow, and the count feels neither bloated nor thin.

Completeness2/5

The workflow covers generation, compliance, review response, and appeals, but four tools explicitly say they run asynchronously and instruct callers to poll for the result, with no polling or result-retrieval tool present in the set. This creates a dead end for core workflows and will likely cause agent failures unless an unlisted resource mechanism exists.

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