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 provide readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds some useful behavioral context ('interactive overview', 'up to 50 tracked keywords'), but does not disclose much beyond that. This is adequate but not rich.
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 sentences, with the primary action front-loaded and a snippet about usage following immediately. Every clause adds value, with no filler or repeated schema data.
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 single-parameter, read-only overview tool with a full output schema, the description is complete. It states when to use, what it shows, and the key constraint of a 'known' app. Combined with strong annotations and output schema, no critical contextual information is missing.
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 the single appId parameter is documented as 'Appskyline app id to render.' The description reinforces this by saying 'when the user provides an Appskyline app id,' but does not add substantial meaning beyond the schema, so baseline 3 is appropriate.
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 uses a specific verb ('Render') with a clear resource ('interactive overview for a known Appskyline app'), and states the content scope ('store identities and up to 50 tracked keywords'). It clearly distinguishes itself as an overview tool rather than a data-retrieval or mutation tool.
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 gives clear context for when to use the tool: 'after listing apps or when the user provides an Appskyline app id.' It doesn't explicitly compare to siblings like get_app or get_store_metadata, but the use-after-listening guidance is enough for an agent to route invocation correctly in most cases.
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 cleanly split by entity: apps, keyords, store listing, reviews, search. Potential confusion exists between get_store_isting and get_store_metadata, and show_app_overview vs list_keywords, but the descriptions are sufficiently specific to guide selection.
The set uses lowercase snake_case verb_noun patterns, with get_X for single resources, list_X for collections, add_keyword, search_store_results, and show_app_overview. The mixture of show/get is minor inconsistency, and list_google_play_reviews is a nice exception, but overall it's cohesive.
The domain is built around app tracking, keyword research, store data, and engagement, and 13-14 tools is well within the sweet spot. Few tools feel obviously redundant, and each covers a distinct part of the ASO workflow.
The core read/query workflows are covered: list apps, list keywords, get rankings/history/volume, fetch store listing/metadata, and search. However, add_keyword is the only keyword mutator — there is no delete_keyword or update_keyword — creating a real dead-end for tracked-keyword management. Also review coverage is limited to Google Play alone.