Game Stats AI

count_over_time

Read-only

Count matches or events bucketed by day, week or month over a time range, oldest bucket first. Pass source and bucket, plus occurred_after / occurred_before (ISO 8601) to bound the range; optional version_id and, for events, event_name_id. Returns each bucket's period-start date and count.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "source": "<source>",
      "bucket": "<bucket>"
    },
    "name": "count_over_time"
  }
}

Input Schema

Name	Required	Description
`bucket`	Yes	Size of each time bucket.
`source`	Yes	Whether to count matches or events.
`version_id`	No	Only count data from this game version.
`event_name_id`	No	For source events only: only count this event name.
`occurred_after`	No	Only count data at or after this time (ISO 8601 date or date-time).
`occurred_before`	No	Only count data at or before this time (ISO 8601 date or date-time).

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 8, 2026

TDQS

A3.9/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, so the safety profile is covered; the description goes further by disclosing output ordering (oldest bucket first), the shape of each result (period-start date plus count), and the ISO 8601 format expected for bounds. It does not mention pagination, bucket-filling for empty periods, or timezone handling, which are the remaining behavioral unknowns for a time-series aggregation.

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

Conciseness5/5

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

Two sentences, front-loaded with what the tool does and its ordering guarantee, followed by the invocation essentials and the return shape. No filler or redundancy.

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

Completeness5/5

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

Although there is no output schema, the description explicitly states what is returned ('each bucket's period-start date and count'), and it covers required params, optional filters, and the conditional events-only parameter. For a six-parameter read-only aggregation, an agent has everything needed to call it correctly.

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 description coverage is 100%, so all six parameters are already documented in the schema, and the description largely restates them (ISO 8601 bounds, events-only event_name_id, optional version_id). It does add a little by grouping required versus optional parameters and their interaction, but nothing the schema does not already convey. Baseline 3 is appropriate.

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 uses a specific verb (count) on a specific resource (matches or events) and adds the defining dimension (bucketed by day/week/month over a time range), which is what separates it from plain counters like count_matches or count_events. It never names those siblings explicitly, so the differentiation is implicit rather than stated.

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

Usage Guidelines3/5

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

Usage is implied rather than prescribed: the agent can infer this is the tool for time-series counts versus a flat total, and it receives concrete guidance that occurred_after/occurred_before bound the range and event_name_id applies only when source=events. There is no explicit 'use this instead of X when...' routing and no stated preconditions or limits.

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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