Ranks the storefronts an app is tracked in by how much is winnable there: how many keywords sit in the cusp band, how many already reach the top ten, and whether the rivals' ratings leave an opening. Answers which market to work on first.
| Name | Required | Description | Default |
|---|---|---|---|
| appId | Yes | App Store app id |
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.
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.
