Who ranks for a phrase, deeper than the ten the first page carries. Use it to find the rivals a first-page reading misses - an app at 40 for a phrase you sit at 60 for is the one to study.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| country | Yes | Storefront code, e.g. us, tr, de | |
| keyword | Yes | The phrase to look up. |
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.
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.
