merchantwords

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

No annotations are provided, so the description must convey behavioral traits. It mentions the batch size limit (up to 1000) but does not disclose whether the operation is read-only, potential rate limits, or error handling for invalid keywords.

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 with no wasted words. It is front-loaded and 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?

No output schema is provided, and the description does not specify the structure of returned data (e.g., how volume and depth are formatted per keyword). Error handling and response format are missing, which is important for a batch operation.

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 100% (both parameters have descriptions). The tool description adds no new meaning beyond the schema; it only reiterates the batch limit already in the schema (maxItems: 1000). Baseline score of 3 is appropriate.

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 'Look up', the resource 'volume and depth', and the scope 'up to 1000 keywords in one call'. This distinguishes it from sibling tools like 'search_keywords' (likely single keyword) and 'related_keywords' (related terms).

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 for bulk keyword lookups but does not explicitly state when to use this tool over alternatives like 'search_keywords' or 'related_keywords'. No exclusion criteria or alternative guidance is provided.

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 fully discloses the tool's read-only behavior and the specific data returned (plan, usage, plans list). It does not contradict annotations and adds transparency about live rate-limits and quotas.

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 two sentences, front-loaded with the core functionality followed by usage guidance. Every sentence adds value, with no 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?

Given zero parameters, no output schema, and the simple nature of account info retrieval, the description fully covers what the tool does and when to use it. It is complete for the complexity 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?

There are no parameters, so the baseline is 4. The description adequately covers the tool's action without needing to elaborate on 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 uses specific verbs ('Show') and resources ('caller's MerchantWords API plan, live rate-limit and bulk-quota usage, and the full list of plans') to clearly define the tool's function. It is easily distinguishable from sibling tools like bulk_keywords or get_serp, which deal with keyword or search data.

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 explicit usage scenarios: 'check remaining quota before a large job, or to tell the user which upgrade unlocks more throughput/volume.' While it does not list exclusions or alternative tools, the provided context is clear and helpful for an agent.

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 bears the full burden. It discloses the tool's function but lacks details on data freshness, completeness, or any side effects. For a simple read-only list, this is adequate but minimal.

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 is front-loaded with the action 'List all supported Amazon marketplaces'. It is concise with no wasted words.

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 a simple purpose, the description is adequate but lacks detail about what 'data coverage' entails (e.g., marketplace IDs, names, available features). For a tool returning a list, more specificity would improve completeness.

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 input schema has no parameters, and schema description coverage is 100%. The description adds no parameter-level detail, but none is needed. Baseline score of 4 is appropriate for zero-parameter tools.

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 it lists supported Amazon marketplaces and their data coverage. The verb 'List' and specific resource 'supported Amazon marketplaces' make the purpose unambiguous. It distinguishes from siblings which focus on keywords, accounts, SERP, or ASINs.

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?

While the description does not explicitly state when to use this tool versus alternatives, the context (zero parameters, sibling tools with different purposes) makes it obvious. There are no exclusions needed, as it's the only tool for listing marketplaces.

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 must fully convey behavioral traits. It states the scope ('page 1') and output fields, but omits important details like pagination support, rate limits, authentication requirements, or behavior on missing/ambiguous keywords.

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 concise sentence with zero filler words. It achieves maximum information density relative to length.

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 tool with two parameters and no output schema, the description covers the core purpose and key output fields. However, it lacks guidance on edge cases, error handling, or data freshness, which would improve completeness.

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 100%, so the schema already documents both parameters (keyword and country). The description adds no extra parameter details, but reinforces the output context. Baseline 3 is appropriate.

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 specifies the action ('Get'), the resource ('products on page 1 of Amazon search results for a keyword'), and the data returned ('prices, stars, and reviews'). This distinguishes it from sibling tools like 'search_keywords' (which may return keyword data) and 'search_asin' (which targets a specific product).

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 the user needs top-page search results for a keyword, but does not explicitly state when to avoid this tool or mention alternatives. For example, it doesn't clarify that it only returns page 1 or suggest using 'related_keywords' for broader keyword exploration.

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 provided; description does not disclose behavior beyond the basic function. Missing details on side effects, authorization needs, rate limits, or 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?

Single sentence with front-loaded verb and resource. No wasted words, efficient and clear.

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?

Adequate for a simple tool with 3 parameters and no output schema, but lacks details on pagination behavior, country code format, and output structure.

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 covers all 3 parameters with descriptions. Description adds 'ASIN co-occurrence' context but doesn't enhance parameter meaning beyond schema. Baseline 3.

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?

Description specifies verb 'Find keywords related to a seed keyword' with method 'via ASIN co-occurrence (Keyword Multiplier)', clearly distinguishing from siblings like search_keywords and bulk_keywords.

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?

Implied usage for finding related keywords via ASIN co-occurrence, but no explicit when-to-use, when-not-to-use, or alternatives like using search_keywords for direct search.

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 must convey behavioral traits. It states it retrieves product details and top keywords, which implies a read operation, but does not mention data freshness, response size, or any rate limits. More detail would be beneficial.

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 with no extraneous words. It efficiently communicates the core functionality.

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 has only two parameters, no output schema, and no annotations, the description is largely sufficient. However, it could briefly mention the nature of 'product details' or that 'top keywords' is a ranked list. Overall, it is nearly complete.

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 100%, so the input schema already documents both parameters. The description adds 'Reverse ASIN lookup' context but does not provide additional meaning beyond what the schema offers (asin pattern, country default).

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 'Reverse ASIN lookup' clearly identifies the verb and resource. It specifies 'get product details and the top keywords it ranks for,' which distinguishes it from sibling tools like search_keywords (which search by keyword) or get_account (which is account-focused).

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?

No explicit guidance on when to use this tool versus alternatives like search_keywords or related_keywords. The description implies use when you have an ASIN, but does not state exclusions or provide context about limitations.

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 burden. It discloses that the tool returns search volume, depth, trend, and history, implying a read operation. However, it omits details like rate limits, authentication, or data freshness, leaving some behavioral traits unclear.

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 with no fluff, effectively front-loading the key purpose. It could be slightly enhanced with structure, but it is appropriately sized for a simple tool.

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 low complexity (2 params, no output schema), the description provides a reasonable overview of the metrics returned. However, it lacks details on the output format or pagination, leaving the agent to infer the structure.

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 coverage is 100%, so the baseline is 3. The description adds no new semantic meaning beyond the schema descriptions for 'keyword' and 'country'; it only summarizes the tool's purpose.

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 'look up' and the resource 'Amazon keyword metrics', specifying outputs like search volume, depth, trend, and 12-month history. It distinguishes from sibling tools like bulk_keywords and related_keywords through this specific scope.

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?

No guidance is provided on when to use this tool versus alternatives such as bulk_keywords or related_keywords. The description does not indicate when not to use it or mention any prerequisites.

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