rankcusp

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

Beyond the annotations, the description reveals that keywords arrive unscored, that scoring is a separate Apple-costing operation on the collector's schedule, and that a null popularity must not be interpreted as zero. This is valuable behavioral context that prevents a likely misinterpretation.

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 three sentences with no filler. The action is front-loaded, and the scoring warning earns its length by explaining an important non-obvious behavior.

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-param mutation with complete schema and useful annotations, the description covers the operation, input format, and post-add behavior. It does not describe return values, and the comma-separated paste ambiguity is a minor gap, so it stops short of a 5.

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 already documents all three required parameters clearly, so the baseline is 3. The description adds the 'list or comma-separated paste' input nuance, though the comma-separated paste phrasing is slightly inconsistent with the array type, which prevents a higher score.

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 first sentence names the action ('Adds'), the object ('keywords'), the scope ('app's tracked set'), and the storefront constraint ('one storefront'). This clearly separates it from read/list siblings like get_keyword and list_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?

The description gives clear context: use this when you want to add keywords to a specific app and storefront. It does not explicitly name an alternative or give a when-not-to-use, but the mutation role is obvious enough from the first sentence.

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 already mark the tool as readOnly, idempotent, and non-destructive. The description adds useful behavioral detail by explicitly stating 'Nothing is submitted anywhere - this only reads the text you pass in' and by disclosing the output concepts: wasted characters, what Apple indexes, and rewritten fields. This goes beyond the annotation metadata without contradicting it.

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 with no filler. The first sentence states the core purpose, the second explains the result and side-effect profile. Every clause earns its place and is front-loaded for quick agent scanning.

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 read-only audit tool, the description covers what it does, what it returns, and that it has no side effects. The main gap is that the required 'country' parameter is not explained in the description, so an agent may not understand why it is needed or how it affects the audit. Still, the overall definition is sufficiently complete for correct 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 input schema already documents all four parameters with 100% coverage, including character limits and the comma-separated keyword field format. The description adds a little context by showing that the fields are audited together and rewritten as a set, but it does not add meaningful details beyond the schema 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 names a specific verb ('Audits') and a concrete target ('draft title, subtitle and 100-character keyword field against Apple's rules'). It also clearly distinguishes the tool from sibling tools like add_keywords, list_keywords, or ranking_keywords, which operate on keywords or rankings rather than auditing metadata compliance.

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 makes the usage context clear: use this when you have a draft App Store metadata set and want to know what Apple will index and what is wasted. It does not explicitly compare to alternatives, but none of the siblings perform this audit function, so the intended use is well implied. It also reassures that nothing is submitted, which signals safe pre-submission use.

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 already declare readOnlyHint, idempotentHint, and destructiveHint false, so the safety profile is covered. The description adds the selection logic and precondiction that the app is tracked in storedfronts. It does not radiative pagination, output format, or data freshness, but with annotation bar this is adequate.

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?

Two sentences with high information density. The action is front-loaded and the closing sentence states the decision value. Only minor redundancy: 'by how much is winnable' and the following explanations overlap slightly.

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 read-Only tool with one parameter and no output schema, the descriprion explains what it ranks, why, and what it answers. The lack of an output schema is partially mitigated by listing the criteria and the synthesis. Return structure isn't defined, but the ranking intent is clear enough.

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% and appId is clearly documented as 'App Store app id'. The tool descriprion adds no parameter-specific detail beyond that. Baseline 3 applies because the schema already carries the burden.

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 and resource: ranks storefronts by opportunity per storefront. The criteria (cusp band, top ten, rival ratings) are concrete. It does not explicitly name sibling tools to differentiate, so it stops short of a perfect 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?

Description clearly frames the use case: deciding which market to work on first. It does not explicitly mention when not to use or name alternatives, so exclusion guidance is absent. Still, the context is unambiguous.

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 already provide readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds the behavioral detail that results are ordered by popularity and restricted to the 11-30 band, but it does not mention pagination, default limit behavior, or return shape.

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 short and front-loaded with the core result set. The second sentence is grammatically awkward ('The work list a metadata edit pays off on most') and introduces ambiguity, so it does not fully earn 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 read-only list tool, the description communicates the result band and ordering, and the schema covers required parameters. However, with no output schema, an undocumented limit parameter, and no mention of alternatives, the description is adequate but has notable 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?

Schema descriptions cover appId and country, but limit is only typed as a number with no explanation. The tool description does not clarify the limit parameter or add any parameter-level semantics beyond the band and ordering.

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 names a specific resource ('keywords in the 11-30 band') and its ordering ('by popularity'), so the object is clear, but it lacks an explicit verb such as 'returns' or 'lists'. It also does not differentiate itself from sibling tools like list_keywords or ranking_keywords beyond the band range.

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 second sentence implies the list is relevant for metadata edits ('The work list a metadata edit pays off on most'), giving some context for when it might be useful. However, it does not explicitly say when to use this tool instead of related keyword tools, and the phrasing is unclear.

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 already cover read-only, idempotent, and non-destructive behavior. The description adds valuable behavioral context beyond annotations: 'Every unmeasured value comes back null, never zero' and the summary of returned fields. There is no contradiction.

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?

Two short, information-dense sentences. The first sentence front-loads the exact resource and output dimensions; the second adds a critical data-interpretation detail. No filler or redundancy.

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 compensates well by naming the return components and the null-versus-zero behavior. It is slightly incomplete in not explicitly guiding the agent toward when this single-keyword tool should be chosen over sibling keyword tools.

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 provides 100% description coverage for all three parameters, so the baseline applies. The description does not add parameter-level detail, but it doesn't need to since the schema already documents appId, country, and keyword clearly.

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 title 'Look up one keyword' plus the description's enumeration of outputs—rank, scores, first page, and targeting verdict—makes the tool's purpose very specific. It is clearly distinguished from siblings like list_keywords and add_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?

Usage is implied: use it when you need details on a single keyword, not a bulk list. However, it never explicitly states when to prefer this over sibling tools like keyword_competitors or ranking_keywords, leaving some selection reasoning to the 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?

Annotations already mark the tool as read-only, idempotent, and non-destructive, so safety is covered. The description adds meaningful behavioral context by explaining that results extend deeper than the first page and that lower-ranked competitors are still relevant.

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 with the core behavior front-loaded and a concrete use-case example in the second sentence. Every sentence adds value with no fluff or repetition.

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 read-only query with annotations already covering the safety profile, the description provides purpose, usage context, and a clear illustrative example. The main gaps are the undocumented limit parameter and the lack of explicit output details, but neither is critical for understanding what the tool does.

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 documents keyword and country, but the limit parameter has only a type and no description. The description hints at depth beyond the first page but never explains how the limit parameter controls that depth, leaving a partial gap at 67% schema coverage.

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 exactly what the tool returns: entities ranking for a phrase beyond the top ten, and it explicitly distinguishes itself from first-page-only views. The phrase 'deeper than the ten the first page carries' makes the scope unambiguous and separates it from sibling ranking tools.

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 an explicit trigger scenario: 'Use it to find the rivals a first-page reading misses,' with a concrete example involving rank positions 40 and 60. It does not name alternative tools or say when not to use it, so it stops short of full routing guidance.

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 already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds useful behavioral context by disclosing that the output contains appId and storefront codes needed by sibling tools, which is especially valuable since no output schema exists.

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?

Two short sentences with no filler. The first sentence states the core function, and the second explains why the output matters, so every sentence 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 zero-parameter, read-only listing tool with annotations covering safety, the description is complete. It tells the agent what the list contains and how the results feed into other tools, leaving no critical gap.

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 tool has zero parameters, so the schema already fully covers the input surface. The description appropriately focuses on output rather than parameters, matching the baseline for parameterless 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 uses a specific verb and resource: it lists the apps and rivals tracked on the account. It clearly communicates what the tool returns, though it does not explicitly contrast itself with sibling tools like list_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?

The description gives clear context by noting that appId and storefront codes used by other tools come from here, implying this is the entry point for identifier lookups. It does not state when not to use it or name alternatives, but the zero-parameter, read-only nature makes the usage context sufficient.

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 already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds useful scope information ('one storefront') and output fields, but does not disclose additional behavioral details like pagination, default ordering, 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?

A single, front-loaded sentence with no filler. It communicates the action, resource, scope, and output metrics efficiently.

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 plus schema annotations provide enough context for basic invocation. Minor gaps remain around offset semantics and the exact output structure, but these are not critical for a read-only list operation already covered by annotations.

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 83%, so most parameters are already well-documented. The description adds no new parameter-level meaning beyond the schema; offset remains undocumented, though its semantics are reasonably self-evident.

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 action and resource: 'Lists an app's keywords in one storefront'. It also specifies what data is returned (rank, popularity, difficulty), which distinguishes it from siblings like get_keyword (singular) and ranking_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?

The description implies its purpose clearly but does not explicitly state when to use it over alternatives such as get_keyword or ranking_keywords. There are no exclusions or direct sibling comparisons, so usage context is only implied.

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 readOnlyHint, idempotentHint, and destructiveHint already declaring safety, the description goes further by disclosing the data source ('pooled record of every search'), the non-existence of an Apple endpoint, and the implication that results are limited to what has been observed. This is meaningul behavioral context that helps the agent set expectations about sparse results, and it contradicts nothing in the annotations.

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?

Two sentences with no waste. The core meaning is front-loaded, and each additional clause earns its place by explaining the data pool and the practical limitation. The structure makes it easy for an agent to quickly grasp both the action and the caveat.

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 well covered by annotations (read-only, idempotent, non-destructive) and a complete input schema. The description supplies the remaining crucial context: it is cross-account, relies on observed data, and may return little for unswept apps. For a read-only query tool with no output schema, this is sufficient for an agent to select and 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 100%, so the baseline is 3. The description adds signifcant value beyond the schema for the appId parameter by specifying it can be any App Store id, not only account-owned, and by explaining that results for an app depend on prior sweep coverage. This nuances 'App Store app id' in a way that directly affects invocation and interpretation. Other parameters (limit, country, maxRank) gain nothing beyond the schema, but the added appId context justifies a 4.

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 precise statement of what the tool does: 'Which keywords an app is already ranking for.' It names the resource (ranking keywords), the scope (per app), and adds a distinguishing capability: it works on any App Store id, including rival apps. This clearly separates it from siblings like list_keywords or get_keyword without needing to reference them explicitly.

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: it can be pointed at any app, including a rival, and it relies on observational data from prior searches. However, it does not explicitly state when to use this tool versus alternatives like keyword_competitors or get_keyword, nor does it give when-not-to-use conditions. Context is clear, but exclusions and explicit alternatives are absent.

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