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Haydebug

roblox-analytics-mcp

by Haydebug

get_experience_overview

Get a Roblox experience's health snapshot: key engagement and monetization metrics compared with the prior period to flag significant shifts.

Instructions

One-call health check for an experience. Pulls the headline KPIs — DAU, MAU, stickiness, visits, peak CCU, session length, playtime, D1/D7/D30 retention, revenue, ARPU, ARPPU, paying users and conversion — for a period, compares each against the immediately preceding period of equal length, and flags the ones that moved sharply.

Start here when asked how a game is doing, then drill in with breakdown_metric_by_segments or get_metric_report.

Each KPI reports how it was aggregated: api-period-total values come from a whole-period query, so user counts are distinct users rather than summed daily figures. This issues roughly two queries per metric against a 30-per-minute budget, so a full comparison run takes about a minute.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endTimeNoExclusive end. Defaults to today's UTC midnight so only complete buckets are returned — pass 'now' if you want the partial current day included.
metricsNoOverride the KPI set. Defaults to: DailyActiveUsers, MonthlyActiveUsers, DauMauStickiness, UniqueUsersWithPlaySessions, Visits, PeakConcurrentPlayers, AverageSessionLengthMinutes, AveragePlayTimeMinutesPerDAU, TotalPlayTimeHours, ForwardD1Retention, ForwardD7Retention, ForwardD30Retention, DailyRevenue, AverageRevenuePerUser, AverageRevenuePerPayingUser, PayingUsers, PayingUsersCVR.
lastDaysNoShorthand for the last N complete UTC days. Cannot be combined with startTime.
startTimeNoInclusive start. RFC 3339 (2026-01-01T00:00:00Z), a date (2026-01-01), or relative (-30d, -12w, -6mo, -2y, -48h, today, yesterday).
universeIdNoRoblox universe (experience) ID, or a saved alias. Optional when a default universe is configured — use get_server_status to check, or find_universes to look one up.
includeDailySeriesNoAlso return the daily time series per KPI. Adds one query per metric.
compareToPreviousPeriodNoCompare against the preceding equal-length window. Default true. Doubles query count.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

No annotations provided, so description supplies the behavioral burden. It discloses query-cost behavior ('roughly two queries per metric... 30-per-minute budget... about a minute') and aggregation nuance ('api-period-total values... distinct users rather than summed daily figures').

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?

Three compact paragraphs, front-loaded with a clear one-sentence summary. Sentences on aggregation and query budget are essential context, not filler. Slightly longer than a minimal description but every sentence earns its place.

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

Completeness4/5

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

Despite complexity (7 params, no output schema, no annotations), the description covers purpose, usage, behavioral traits, and cost implications. It doesn't detail exact return structure, but the KPI list and flag behavior give enough for an agent to know what to expect.

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?

Input schema covers all 7 parameters with detailed descriptions, so the baseline is 3. The description adds global context (query budget, defaults) but does not elaborate any parameter beyond what the schema already documents, such as the metrics override list and compare flag.

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?

Description states 'One-call health check for an experience' with specific verbs (pulls, compares, flags) and resource. It distinguishes from siblings by instructing 'Start here... then drill in with breakdown_metric_by_segments or get_metric_report.'

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

Usage Guidelines5/5

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

Explicit guidance: 'Start here when asked how a game is doing' and points to alternatives for deeper analysis. It also frames the tool as the initial high-level overview, clearly separating it from drill-down 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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