ListingGood MCP

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

With no annotations, the description carries the full burden and discloses key behaviors: it does not deduct star points (no cost), sends a confirmation email and records lead if email is provided, and uses deterministic rules. It could mention more about response format or error behavior, but the listed traits are valuable and transparent.

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 front-loaded with the tool's purpose and key selling points. It uses a colon to introduce the action and semicolons to separate parameter explanations without redundancy. Every sentence contributes meaningful 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 4 parameters, no annotations, and no output schema, the description provides a strong overall picture: purpose, parameters, side effects, and return type (scores and suggestions). It lacks explicit details on output structure or edge cases, but the given information is sufficient for basic invocation.

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 schema has 0% coverage, but the description explicitly explains each parameter: text (title+bullets+description), marketplace (US/DE/JP/AE/SA etc.), lang (zh/en), and email (optional, triggers confirmation email and lead recording). This fully compensates for the schema's lack of descriptions.

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 tool performs a readiness check on listing text using deterministic rule scanning, returning dual-dimension scores (compliance health and AI readability). It distinguishes from siblings like compliance_check by adding the AI readiness dimension and emphasizing it's free with no API key.

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 provides context (free, no API key, lead generation hook) and implies usage for checking listing readiness. However, it does not explicitly state when to use this tool versus alternatives such as compliance_check or compliance_scan, nor does it mention any exclusions or preferences.

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 and does disclose two important behavioral traits: it consumes 3 stars and is asynchronous. However, it does not mention any other side effects, possible errors, or required permissions, leaving ambiguity about the operation's full impact.

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 a single, front-loaded sentence stating the purpose and key behavioral notes, followed by a compact parameter list. Every word earns its place; there is no redundancy or filler.

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?

The tool is moderately simple with three parameters and no output schema, so the description provides the core purpose and parameter semantics. However, it lacks usage guidance and details on how the asynchronous result is delivered, which would be helpful given the star cost. It is adequate but has clear gaps.

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 schema has no parameter descriptions (0% coverage), but the tool description explicitly lists each parameter with its meaning: text is the original review text, marketplace is the site code, and lang is zh/en. This adds significant value beyond the bare schema, though allowed values for marketplace are not specified.

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 tool analyzes an Amazon negative review and provides root cause and response suggestions, which is a specific verb+resource+outcome. However, it does not explicitly differentiate from sibling tools like generate_listing or compliance_check, so it falls short of a 5.

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 implies usage when you have a negative review but provides no explicit guidance on when to use this tool vs alternatives, nor any exclusions or prerequisites. No alternatives are named, and the context of sibling tools is not addressed.

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 disclosure burden. It transparently states the tool is a quick scan for obvious issues, not exhaustive, and notes the API Key requirement and free (non-deducting) nature. This adds meaningful behavioral context beyond the tool name, though it doesn't describe the return format.

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 concise, two sentences long, front-loaded with the core purpose and key requirement (API Key). It includes parameter explanations compactly without redundancy, every sentence earning 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?

Given the tool's simplicity and lack of output schema, the description covers purpose, usage timing, parameter semantics, and basic behavior. However, it omits details about the return structure or how to interpret results, which would be useful for an agent. Overall, it's mostly complete but with a gap in output expectations.

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%, but the description compensates by explicitly explaining each parameter: text is the listing copy, lang specifies zh/en, and category is optional with examples like electronics/apparel. This adds significant semantic value over the raw schema types and defaults.

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 free compliance initial check that scans for obvious red-line words and category risks. It explicitly positions this as a pre-generation step, distinguishing it from sibling tools like compliance_scan by emphasizing '初检' (initial check).

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 provides clear usage context: use before generation, requires an API Key, and does not deduct star points. It implies this is a lighter, pre-screening tool rather than a comprehensive scan, though it does not explicitly name alternatives or exclusion criteria.

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?

No annotations are provided, so the description carries the full burden. It discloses the cost (consumes 3 stars), the asynchronous nature ('异步'), and the knowledge-base-driven approach. It also specifies parameter constraints (images base64, max 5, <4MB). It does not describe the return format or how to handle the async result, but the key behavioral traits are covered.

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 a single compact sentence front-loaded with the core purpose, followed by a terse parameter list. Every sentence earns its place, with no redundant filler. The structure is easy to scan and digest.

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?

The description covers the tool's function, parameters, and constraints, but omits the output format and the async workflow (e.g., whether a job ID is returned and how to fetch the final report). Given the tool's complexity (cost, async, multiple params) and lack of output schema, this leaves a notable gap for the agent.

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 schema has zero descriptions, so the description fully compensates by explaining every parameter: text (listing title/bullets/original text), marketplace (site code), category (optional), lang (zh/en), and images (optional, max 5 base64, <4MB each). This adds significant meaning beyond the bare 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 clearly states that this tool performs a deep compliance check ('深度合规体检') and generates a knowledge-base-driven risk report covering prohibited words, intellectual property, categories, GPSR, etc. The verb 'scan' and the specific coverage areas distinguish it from sibling tool 'compliance_check', making its purpose 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?

Usage is implied by the word '深度' (deep) and the cost of 3 stars, suggesting it is for thorough compliance checks. However, there is no explicit guidance on when to use this tool versus alternatives, nor any mention of 'when not to use' or exclusions.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the API key requirement and cost details ('免费，需 API Key，不扣星点'), but it does not disclose whether the operation is read-only, potential side effects, rate limits, or failure behavior. For an AI generation tool, this is insufficient.

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 a single sentence that packs purpose, cost notes, and parameter definitions. It is somewhat cluttered but still readable. The cost info is extra but short, and the purpose is front-loaded. It earns a middle score for being concise yet slightly haphazard.

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?

With no output schema, the description should clarify the return format. It partially does by saying the output is '结构化表单字段', but it lacks detail on the structure or error handling. Given the tool's simplicity (2 params, no nested objects), it is minimally adequate but not comprehensive.

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 schema has 0% description coverage, but the description compensates by explaining both parameters: sentence is a product description of 4-1000 characters, and lang is limited to 'zh/en'. This adds meaning beyond the raw schema and clarifies constraints.

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 tool's purpose: '把口语化一句产品描述拆成结构化表单字段' (split a colloquial product description into structured form fields). This specific verb and resource distinguish it from sibling tools, though it doesn't explicitly name alternatives.

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 implies usage context via '供后续生成使用' (for subsequent generation), suggesting when to use it. However, it doesn't explicitly state when to use this tool versus alternatives like generate_listing, nor does it provide exclusions or prerequisites beyond requiring an API key.

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 burden of behavioral transparency. It discloses that the operation is asynchronous, billed per marketplace, and outputs specific components (title, bullets, description). It also mentions optimization coverage. This is more transparent than typical tool descriptions, though it doesn't cover all possible behavioral aspects like error handling or rate limits.

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 a single, information-dense sentence that leads with the core purpose and immediately follows with key constraints (billing, async) and parameter definitions. There is no wasted text; every phrase adds value. It is front-loaded and easy to parse.

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 the tool's complexity (5 params, no output schema, no annotations), the description covers the essential context: purpose, output format, special behaviors (async, billing, character limits), and parameter meanings. It lacks some details about expected response structure or failure scenarios, but is largely complete for a generation tool.

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 schema has 0% description coverage, so the parameter semantics rely entirely on the description. It explicitly explains each parameter: cn_name (required), sku (required), marketplaces (optional, defaults to all 9), price (optional), and lang (zh/en). This gives the agent sufficient understanding to invoke the tool correctly, though not deeply detailed.

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 tool's function: generating high-conversion Amazon Listings, with specifics like title, bullets, and description, plus coverage of multi-marketplace character limits and A9 optimization. This distinguishes it from sibling tools like compliance_check or analyze_review, which have different purposes.

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 provides clear context for when to use the tool: when generating an Amazon listing. It also mentions billing per selected marketplace and asynchronous execution, which are key usage conditions. However, it does not explicitly state when not to use it or name alternative tools, so it falls short of a 5.

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?

No annotations are provided, so the description carries the full burden. It discloses that the tool consumes 10 stars and is asynchronous, which are critical operational traits. However, it does not explain how results are retrieved or failure behavior, leaving a minor gap.

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 concise, with two sentences covering purpose, cost, async behavior, and parameter meanings. It is slightly run-on in the parameter list but every piece is necessary, making it efficient.

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?

With no output schema and no annotations, the description should explain what the agent gets back. It states the output is a submittable letter, but for an async tool it does not clarify whether it returns a job ID, how to poll for the result, or error handling. This is a notable gap for a 4-parameter tool.

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. It does so by explaining each parameter: 'text' as the violation notice content, 'marketplace' as site code, 'lang' as zh/en, and 'violation_type' with concrete examples. This adds substantial meaning 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 clearly states a specific action: generate a submittable POA appeal letter based on Amazon violation notifications or delisting emails. It distinguishes the tool from siblings like compliance_check or generate_listing by focusing on the POA generation use case.

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 implies when to use the tool: when the user has an Amazon violation notification or delisting email. Clear context is provided, but it does not explicitly state when not to use it or mention alternatives, so it stops short of full guidance.

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