list_appsList all Appskyline apps the authenticated user has access to. Returns a Markdown table of id, name, default locale, and the per-store ids (iOS App Store, Google Play, Microsoft Store) when set.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| apps | Yes | ||
| status | Yes | ||
| truncated | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, non-destructive behavior. The description goes beyond annotations by explaining the Markdown table return format and which fields are included, and the 'when set' qualifier for per-store ids is a useful behavioral detail.
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 the core purpose front-loaded and the return format described in one compact follow-up. Every phrase adds useful information and there is no filler.
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 parameterless, read-only, output-schema-bearing list tool, the description is complete: it defines the scope of results, names the return format, and lists the exact fields. No additional detail seems necessary for an agent to call it correctly.
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 tool has zero parameters and the schema documentation coverage is 100%, so there is nothing for the description to clarify about parameters. The description correctly focuses on behavior and result format instead.
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 ('List all...apps'), the specific resource ('Appskyline apps'), and the access scope ('authenticated user has access to'). It also specifies the exact return content, which fully disambiguates it from sibling tools like get_app.
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 enumeration context: it lists all apps the user can access, which implies a high-level overview use case. It does not explicitly name an alternative for retrieving a single app's details, but the scope is clear enough that an agent could infer when this tool fits.
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.
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.