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Glama

ListingGood MCP

by io.github.ryanyang828

generate_listing

Generate a high-conversion Amazon listing - title + bullets + description (async; 1 credit per marketplace).

Produces A9-optimized copy that respects per-marketplace character limits. Use this to create
a full listing from product facts. Not free - deducts 1 credit per selected marketplace and
runs asynchronously, poll for the result. Read-only: it drafts text and does not publish.

Args:
    cn_name: product Chinese name (required).
    sku: product SKU (required).
    marketplaces: optional list of marketplace codes; defaults to all 9.
    price: optional product price.
    lang: zh or en (default en).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skuYes
langNoen
priceNo
cn_nameYes
marketplacesNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -{
-  "additionalProperties": true,
-  "description": "Structured result (scores, issues, suggestions, or generated content). Shape varies per tool; clients should not strictly validate due to additionalProperties.",
-  "type": "object"
-}New value: +null
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "description": "Structured result (scores, issues, suggestions, or generated content). Shape varies per tool; clients should not strictly validate due to additionalProperties.",
+  "type": "object"
+}
  3. First observed

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations available, the description carries the full burden and does exceptionally well. It discloses async behavior ('runs asynchronously, poll for the result'), cost ('deducts 1 credit per selected marketplace'), read-only nature ('does not publish'), and per-marketplace character limits. These are all behavioral traits beyond what any schema provides.

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. The first line captures the core function, the second adds key constraints, and the Args section is cleanly formatted. Every sentence contributes meaningful, non-redundant information.

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 tool with no output schema, the description covers the main contextual needs: output format, async behavior, credit cost, defaults, and read-only status. Minor gaps remain, such as the exact polling interface and an enumeration of marketplace codes, but these are not critical for invoking the tool correctly at a high level.

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. The Args section gives meaning, required status, defaults, and allowed values for every parameter (e.g., 'cn_name: product Chinese name (required)', 'marketplaces: optional list of marketplace codes; defaults to all 9', 'lang: zh or en (default en)'). This fully covers all 5 parameters.

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 clearly states the verb 'Generate' and the resource: 'a high-conversion Amazon listing - title + bullets + description'. It distinguishes this tool from siblings by describing a specific, unique output type, so an agent can easily understand its purpose.

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?

It explicitly says 'Use this to create a full listing from product facts', giving a clear when-to-use condition. It also notes important constraints like cost and async execution. However, it does not explicitly name alternative tools or give when-not-to-use guidance, so it falls just short of full marks.

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

A4.4/5.0
Disambiguation4/5

Most tools have clearly distinct roles: generation, compliance, review analysis, and POA drafting are separated. The two readiness checks (agent_ready_check vs ai_readiness_check) and the two compliance tools (compliance_check vs compliance_scan) could be confused by name alone, but their descriptions explicitly clarify scope and intended use.

Naming Consistency3/5

Four tools consistently use verb_noun naming (analyze_review, fill_from_sentence, generate_listing, generate_poa), while the other four use compound noun phrases (agent_ready_check, ai_readiness_check, compliance_check, compliance_scan). All names are snake_case and readable, but the mixed verb/noun convention is noticeable and not fully consistent.

Tool Count5/5

Eight tools is well within the ideal 3-15 range and each tool maps to a distinct step in the listing lifecycle: input expansion, listing generation, compliance pre-check, deep compliance audit, AI/agent readiness scoring, review response, and appeal drafting. No tool feels redundant or out of place.

Completeness4/5

The toolset covers the main listing workflow well: expanding rough input, generating optimized listings, checking compliance, auditing deeply, scoring AI readiness, handling reviews, and drafting POAs. A minor gap is the lack of a dedicated tool for editing or optimizing an already-existing listing, but the core lifecycle is otherwise well covered.

Resources