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ListingGood MCP

by io.github.ryanyang828

generate_poa

Draft a submission-ready Plan of Action (POA) from an Amazon violation notice (async; 4 credits).

Converts a suspension or removal email into a structured POA appeal. Use this when a listing
or account is suppressed. Not free - deducts 4 credits and runs asynchronously, poll for the
result. Read-only: it drafts text and does not submit anything to Amazon.

Args:
    text: violation notice or removal email text (required).
    marketplace: marketplace code (default US).
    lang: zh or en (default en).
    violation_type: optional, e.g. ip_complaint / authenticity / policy.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoen
textYes
marketplaceNoUS
violation_typeNo

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden. It discloses that the operation is async, costs 4 credits, is read-only, and does not submit anything to Amazon. This is strong transparency, though it omits details about what the returned result looks like or what 'poll for the result' entails.

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, then usage context, then args. It is mostly efficient, though 'Converts a suspension or removal email into a structured POA appeal' partially restates the first sentence. Still, every important detail found a place without bloat.

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?

Given no output schema, the description usefully covers async behavior, cost, read-only nature, and all parameters. It is complete enough for an agent to invoke correctly, but the missing description of the polling mechanism/result shape leaves a small ambiguity about what to expect after submission.

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 fully compensates by listing all four parameters with meanings, defaults, and examples. It clarifies the required text input, default marketplace, language options (zh/en), and optional violation_type examples. This adds significant value beyond the schema's bare titles.

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 uses a specific verb and resource: 'Draft a submission-ready Plan of Action (POA) from an Amazon violation notice.' It clearly differentiates this tool from siblings like compliance_check or analyze_review by focusing on converting a violation notice into an appeal, and even clarifies it does not submit anything.

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 explicit when-to-use guidance: 'Use this when a listing or account is suppressed.' It also notes operational constraints such as the 4-credit cost, asynchronous execution, and the need to poll for the result. It does not name alternative tools explicitly, but the condition is clear enough for an agent to decide.

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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