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ListingGood MCP

by io.github.ryanyang828

fill_from_sentence

Expand a one-sentence product description into structured listing fields (free; API key; no credits).

Turns casual, spoken product copy into structured fields (title, bullets, features) that feed
the listing generator. Use this as a low-friction starting point when you only have a rough
sentence. Do NOT use it to produce a final optimized listing - use generate_listing for that.
Read-only; free; no credits deducted.

Args:
    sentence: one-sentence product description, 4-1000 characters (required).
    lang: zh or en (default en).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoen
sentenceYes

TDQS

A4.5/5.0
Behavior4/5

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

With empty annotations, the description carries the full burden. It discloses key behavioral traits upfront: 'free; API key; no credits' and later 'Read-only; free; no credits deducted.' These indicate the operation is read-only, requires authentication, and has no billing impact. This goes beyond minimal disclosure, though it does not mention error handling or rate limits, which keeps it below a perfect score.

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: the first line states the purpose and key operational notes, followed by usage guidance and an args list. Minor redundancy exists (free/API key/no credits appears twice), but overall it's concise and every sentence contributes meaningful context.

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 2-parameter, read-only transform with no output schema and no annotations, the description provides ample guidance: it explains the use case, names the alternative for final listings, specifies parameter constraints, and hints at output structure ('title, bullets, features'). The only gap is a precise definition of the return format, but that is partially covered by the output description.

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

Parameters4/5

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. The 'Args' section adds real meaning: 'sentence: one-sentence product description, 4-1000 characters (required)' and 'lang: zh or en (default en).' This specifies length constraints, requiredness, allowed values, and default, which the bare schema (only type and title) lacks. Clear value-add beyond the schema.

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 clear purpose: 'Expand a one-sentence product description into structured listing fields' and elaborates into 'structured fields (title, bullets, features)'. It explicitly contrasts with the sibling generate_listing by saying 'Do NOT use it to produce a final optimized listing - use generate_listing for that.' This makes the tool's distinct role unambiguous.

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?

The description gives explicit when-to-use and when-not-to-use guidance: 'Use this as a low-friction starting point when you only have a rough sentence' and 'Do NOT use it to produce a final optimized listing - use generate_listing for that.' It names the alternative tool and the condition for choosing it, leaving nothing to inference.

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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