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ListingGood MCP

by io.github.ryanyang828

compliance_check

Quick pre-publish compliance gate before generating a listing.

Fast, free scan for obvious red-line words and category risks. Returns a shallow
pass/fail-style result, not a full audit.

Use this as a cheap pre-check right before generation. Do NOT use it for a complete risk
report - use compliance_scan for the deep knowledge-base audit. Read-only; requires an API
key; no credits deducted.

Args:
    text: listing copy (required).
    lang: zh or en (default en).
    category: optional category hint, e.g. electronics or apparel.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoen
textYes
categoryNo

TDQS

A4.9/5.0
Behavior5/5

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

With empty annotations, the description carries the full burden and does so well. It discloses read-only behavior, API key requirement, zero credit deduction, and the shallow nature of the result. These are meaningful behavioral traits beyond the schema that help an agent set expectations.

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?

The description is well-structured and front-loaded with the core purpose, followed by usage guidance and parameter details. There is minor redundancy between 'Quick' and 'Fast' and the repeated emphasis on 'shallow', but every section earns its place.

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?

For a simple 3-parameter tool with no output schema, the description covers what it does, when to use it, what it does not do, access requirements, cost implications, and parameter semantics. The pass/fail-style result is described adequately for an agent to invoke it 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%, so the description must compensate, and it does. It explains text as required listing copy, lang as zh or en with default en, and category as an optional hint with examples, adding meaning the schema lacks.

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 states a specific verb and resource: it is a quick pre-publish compliance gate that scans for red-line words and category risks. It also explicitly contrasts itself with compliance_scan by calling this a shallow pass/fail-style check rather than a full audit, so an agent can distinguish it from siblings.

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?

Usage is explicitly scoped: use it as a cheap pre-check right before generation, and do NOT use it for a complete risk report. It names the alternative (compliance_scan) for the deep knowledge-base audit, leaving no ambiguity about when to choose which 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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