One keyword: its rank, its scores, its first page and the verdict - should it be targeted, and why. Every unmeasured value comes back null, never zero.
| Name | Required | Description | Default |
|---|---|---|---|
| appId | Yes | App Store app id | |
| 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 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.
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.
