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billylo1

aptabase-mcp

by billylo1

get_top_app_versions

Sort app versions by unique session count. Apply filters like OS, period, and event name to focus on specific user segments.

Instructions

Top app versions by unique sessions for the period.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
appIdYesAptabase app id (from list_apps)
osNameNoFilter by OS name (e.g. iOS, Android, macOS)
periodNoDashboard period preset. Prefer this over startDate/endDate. Defaults to 24h for stats tools when neither period nor dates are set.
endDateNoISO end datetime (use with startDate if not using period)
buildModeNoRelease or debug build mode (debug queries appId_DEBUG)release
eventNameNoFilter by event name
startDateNoISO start datetime (use with endDate if not using period)
appVersionNoFilter by app version
countryCodeNoFilter by ISO country code
deviceModelNoFilter by device model
granularityNoOverride bucket size; auto-derived from period/range when omitted
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the output metric and period, but fails to mention side effects (or lack thereof), default behavior, sorting, filtering, or any other operational characteristics. For a stats tool, this is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, concise sentence with no redundancy or fluff. It is efficient, though it may be too terse given the tool's complexity. Still, it scores well on pure conciseness and structure.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (11 parameters), lack of output schema, and absent annotations, this description is severely incomplete. It does not explain the output format, default period, or how filters interact with the results. An AI agent would struggle to understand what to expect from this tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage for all 11 parameters, so the baseline is 3. The tool description adds no parameter-specific information beyond what the schema already provides, which is acceptable but not additive.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly indicates the resource (app versions) and metric (unique sessions), and the period context. It distinguishes from siblings by focusing on app versions specifically, though it lacks an explicit verb like 'get' or 'list', relying on the tool name to imply retrieval.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives such as get_top_os_versions or get_metrics. There is no mention of typical use cases, limitations, or when to prefer this over other stats tools.

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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