show_app_overviewRender an interactive overview for a known Appskyline app. Use this after listing apps or when the user provides an Appskyline app id. It shows store identities and up to 50 tracked keywords.
| Name | Required | Description | Default |
|---|---|---|---|
| appId | Yes | Appskyline app id to render |
| Name | Required | Description | Default |
|---|---|---|---|
| app | Yes | ||
| keywords | Yes | ||
| storeCount | Yes | ||
| keywordCount | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already signal readOnly=true, idempotent=true, and destructive=false, so the description only needs to add value beyond those. It adds meaningful behavioral context: the app must be known, output is interactive overview, and keyword display is capped at 50. No contradiction with 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, no filler, and the operational context is placed first: what app is rendered, when to invoke the tool, and what the output contains. Every sentence contributes.
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 has a single fully-documented parameter, strong annotations, and an output schema. The description adds the key contextual facts not present in structured data (known app requirement, interactive rendering, 50-keyword cap), so nothing essential is missing 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?
Schema coverage of appId is 100%, so the schema already documents the parameter. The description meaningfully adds that appId should come from a prior listing step or from a user-supplied known app id, which helps the agent understand where the value comes from. This goes beyond a bare restatement of the schema.
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 clearly states the action ('Render an interactive overview'), the target resource ('a known Appskyline app'), and the actual content shown ('store identities and up to 50 tracked keywords'). This distinguishes it from sibling tools like get_app, get_store_metadata, and get_keyword_overview, which are narrower or more detailed.
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 explicitly says to use this tool after listing apps or when the user provides an Appskyline app id. This gives a clear activation context. It does not explicitly list when not to use it, but the stated conditions are sufficiently discriminating for an agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Most tools map to clearly distinct resources and actions, such as listing apps, getting keyword history, fetching store listings, and searching store results. A couple of adjacent getters, particularly show_app_overview vs get_app and get_store_metadata vs get_store_listing, could be confused, but their descriptions are strong enough to keep an agent on the right path.
Tool names follow a uniform verb_noun pattern throughout: add_, delete_, get_, list_, search_, show_. Resources such as app, keyword, store, and overview are consistently placed after the verb, making the set predictable and easy to reason about.
Fourteen tools serve the ASO and store-insight domain well without feeling bloated. The count covers app discovery, keyword tracking, store metadata, engagement summaries, live store search, and reviews, so each tool earns a place.
The core workflows are well covered: list apps, get app details, track and delete keywords, check current rank and historical rank, look up search volume and difficulty, read store listings, and pull Google Play reviews. Obvious gaps are an update-keyword operation and App Store review listing, but most primary use cases do not hit dead ends.