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ListingGood MCP

by io.github.ryanyang828

analyze_review

Analyze a negative Amazon review for root cause and a suggested response (async; 2 credits).

Extracts the underlying issue from a critical review and drafts a brand-appropriate response
angle. Use this after a negative review appears, to decide how to reply. Do NOT use it to
generate a listing or an appeal - use generate_listing or generate_poa for those. Read-only;
deducts 2 credits; runs asynchronously, poll for the result.

Args:
    text: the negative review text (required).
    marketplace: marketplace code (default US).
    lang: zh or en (default en).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoen
textYes
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 carries the full burden. It discloses that the tool is read-only, deducts 2 credits, and runs asynchronously with polling required—essential behavioral facts beyond the schema.

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 compact and well-structured: purpose, usage guidance, behavioral notes, then an Args block. Every sentence earns its place without redundancy, and the most critical information is front-loaded.

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 3-parameter async tool with no output schema and no annotations, the description covers purpose, selection criteria, behavior, cost, async pattern, and parameter semantics. An agent has everything needed to select and invoke it correctly; no essential context is missing.

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?

The description compensates for 0% schema coverage by explaining each argument: text is the negative review text, marketplace is a code (default US), and lang is 'zh or en' (default en). It adds semantic meaning that the bare 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?

States a specific verb ('Analyze'), resource ('negative Amazon review'), and outcome ('root cause and a suggested response'), with the async/credit context front-loaded. It also distinguishes itself from siblings by explicitly naming generate_listing and generate_poa for different tasks.

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?

Explicitly states when to use it ('after a negative review appears, to decide how to reply') and when not to ('Do NOT use it to generate a listing or an appeal'), naming the sibling alternatives. This leaves no ambiguity about tool selection.

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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