ListingGood MCP

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

Annotations are empty, so the description fully carries the burden of behavioral disclosure. It states the tool is free, deterministic, rule-based, read-only, requires no API key, and deducts no credits. It also discloses the exact scoring formula and that it returns actionable suggestions, giving agents a clear model of what invoking it will do.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose and deliverables, then moves to usage guidance and then parameters. Every sentence adds value: the formula, the exclusion of compliance_scan, the cost/access characteristics, and the parameter meanings are all necessary for correct invocation.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and no annotations, the description provides everything needed to call the tool correctly: purpose, input format, parameter domains, default values, output composition, and when not to use it. It even explains why email might be supplied, covering the lead-capture side effect clearly.

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

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 for the schema's silence. It does this thoroughly: text is defined as 'raw listing title + bullets + description,' marketplace lists valid codes, lang specifies zh/en, and email is described as an optional lead-capture field. This exceeds what the bare schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Score how likely Amazon's AI (Rufus, COSMO) is to recommend a listing.' It clearly distinguishes itself from sibling compliance_scan by labeling itself a fast baseline check, so an agent can tell which tool to use.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit timing: 'Use this as a fast baseline BEFORE generating or editing a listing.' It also names the alternative for deeper audits: 'Do NOT use it for a full compliance report - use compliance_scan for the deep knowledge-base audit.' This leaves no ambiguity about when to choose this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly states the tool is async, read-only, deducts 2 credits, and requires polling for results. It also describes the output as a thorough written risk report, providing valuable behavioral context 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-organized and front-loaded, with the core purpose stated first, followed by usage guidance, behavior, and a clean Args list. The phrase 'backed by a private 15-year compliance knowledge base' is slightly promotional and not strictly necessary, but it does add credibility context. Overall, it is concise without waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 5 parameters, no output schema, and no annotations, this description covers the essential operational context: what it does, when to use it, what it costs, how it executes (async), how inputs are constrained, and what kind of result to expect. Nothing critical for correct invocation is missing.

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

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must fully compensate for all parameters. It does: text is marked required, marketplace gets a default and purpose, category is described as a hint, lang specifies allowed values (zh/en) and default, and images specifies the exact format (data:image base64), quantity (up to 5), and size limit (<4MB).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as a deep compliance audit that produces a written risk report, listing specific coverage areas (prohibited words, IP, category mismatches, GPSR). It explicitly distinguishes itself from the sibling compliance_check by stating this is not for a quick pre-publish check, giving a clear basis for selection.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this tool ('when you need a complete, actionable compliance report') and when NOT to use it, directing the agent to compliance_check for quick pre-publish checks. This directly names the alternative, which is ideal for routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.