The keywords in the 11-30 band, ordered by popularity. The work list a metadata edit pays off on most.
| Name | Required | Description | Default |
|---|---|---|---|
| appId | Yes | App Store app id | |
| limit | No | ||
| country | Yes | Storefront code, e.g. us, tr, de |
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.
Add one secure layer between your agents and this server.
Each tool targets a distinct resource or analytical view: tracked keywords, single-keyword details, cusp-band work lists, competitor rankings, storefront priorities, and metadata auditing. The descriptions clarify the boundaries between overlapping-sounding tools like list_keywords, cusp_actions, and ranking_keywords.
Several tools follow a clean verb_noun pattern (add_keywords, get_keyword, list_keywords, list_apps, audit_metadata), but others are noun phrases or gerunds (country_priorities, cusp_actions, keyword_competitors, ranking_keywords). Names are readable and meaningful, but the pattern is not consistent across the set.
Nine tools is a well-scoped size for an App Store keyword and ASO research server. Each tool covers a distinct capability without feeling redundant or padded.
The read-side surface is strong: listing apps, keywords, rankings, competitors, and storefront priorities are well covered. However, the tracked-keyword lifecycle is one-directional: add_keywords exists but there is no remove_keywords or update_keywords, leaving agents unable to prune or correct a tracked set.
