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billylo1

aptabase-mcp

by billylo1

get_periodic_stats

Retrieve time-series metrics for users, sessions, and events, bucketed by hour, day, or month to generate charts and analyze trends.

Instructions

Time-series stats (users, sessions, events) bucketed by hour/day/month for charts.

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 disclosing behavior. It only mentions the metric types and bucketing, but does not explain defaults like the period preset, required appId, return format, or how filters interact. This is minimal for a tool with 11 parameters and no output schema.

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 sentence that is efficient and front-loaded with the core purpose. However, it is somewhat under-specified for a tool with this complexity, though conciseness itself is not an issue. It delivers the key message without waste.

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 params, many filter options, no output schema) and the existence of many sibling tools, this description is too minimal. It does not cover the full scope of what the tool does, the period presets, or the default behavior, leaving significant gaps for an agent to correctly select and invoke it.

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?

Schema coverage is 100%, so the baseline is 3. The description adds no additional meaning for the parameters; it only mentions output metrics (users, sessions, events), which are not params. Thus it does not enhance or clarify the parameter semantics beyond what the schema already provides.

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

Purpose5/5

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

The description states a specific action (get) and resource (time-series stats for users, sessions, events) with explicit bucketing by hour/day/month for charts. This clearly differentiates it from sibling tools like get_metrics or get_top_events by emphasizing the periodic, chart-oriented nature.

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

Usage Guidelines4/5

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

The description provides clear context for when to use this tool: when you need time-series stats bucketed by period for charting. It does not explicitly mention when not to use it or name alternatives, but the context is sufficient to imply its intended use among the many sibling 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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