Game Stats AI

aggregate_attribute

Read-only

Summarize one numeric attribute with count, sum, average, minimum and maximum over a live scope. source is Match, MatchPlayer, MatchEvent, Player, or Event; attribute_name must be a numeric attribute (data_type integer or float) at that level from list_attribute_types (for Event, also pass event_name_id). Optional filters: version_id, occurred_after, occurred_before (ISO 8601), except on Player.

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>",
      "attribute_name": "<attribute_name>"
    },
    "name": "aggregate_attribute"
  }
}

Input Schema

Name	Required	Description
`source`	Yes	What the numeric attribute lives on.
`version_id`	No	Only include data from this game version.
`event_name_id`	No	Required when source is Event: the event name from list_event_names.
`attribute_name`	Yes	The numeric attribute to summarize (from list_attribute_types at this level).
`occurred_after`	No	Only include data at or after this time (ISO 8601 date or date-time).
`occurred_before`	No	Only include 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

A3.8/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=true, so the safety profile is covered. The description adds genuine context beyond that: the result includes specific aggregates, the scope is 'live', and filter parameters are rejected for Player. It does not clarify return structure (no output schema exists) or what 'live scope' implies about data freshness, leaving a meaningful gap for a read-only aggregation tool.

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?

Two dense sentences, front-loaded with purpose and the output metrics before constraints. Nothing is redundant, though the second sentence packs several conditional rules together, which slightly reduces scannability.

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?

For a six-parameter aggregation tool with no output schema, the description covers the computed metrics, the source enum semantics, prerequisite lookups, filter applicability, and one important exclusion (Player). Remaining gaps are minor: the meaning of 'live scope' and whether time filters apply across all non-Player sources equally.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3, but the description adds cross-parameter meaning not encoded in the schema: filters do not apply when source is Player, and Event requires event_name_id in addition to attribute_name. It also ties attribute_name validity to list_attribute_types and a numeric data_type. It does not add format detail for occurred_after/before beyond ISO 8601, which the schema already states.

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?

Opens with a specific verb and resource: 'Summarize one numeric attribute' and names the exact output metrics (count, sum, average, minimum, maximum) plus scope ('live scope'). This clearly separates it from sibling report/histogram tools, though it never names an alternative like attribute_histogram or group_by_attribute, so differentiation is implied rather than explicit.

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?

It states prerequisites well: attribute_name must come from list_attribute_types and be numeric (integer/float), and Event source additionally requires event_name_id. It also declares the optional filters and the exclusion 'except on Player'. What it lacks is explicit routing guidance against sibling tools that compute distributions or groupings.

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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