VGC Teambuilder

Get the most-used Pokémon

get_top_usage

Read-onlyIdempotent

Rank the most-used Pokémon in a regulation by the share of ladder teams they appear on, from the most recent month of usage statistics loaded for that regulation (the month itself is not reported; get_usage_trend shows dated history). Use it to see what a team should expect to face. Mega Evolutions are ranked as separate entries.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_top_usage"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No	Maximum entries to return (1–50). Defaults to 20
`format`	No	Regulation id from list_formats. Defaults to "champions-regmc" (Pokémon Champions — Regulation M-C).

Output Schema

Name	Required	Description
`format`	Yes	Regulation id the result applies to
`battles`	Yes	Battles in the month's sample
`pokemon`	Yes
`formatName`	Yes	Display name of the regulation
`pokemonCount`	Yes	Distinct Pokémon recorded

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 13, 2026

TDQS

A4.1/5.0

Behavior4/5

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

With readOnlyHint and idempotentHint already supplied by annotations, the description adds real context not in annotations: only the most recent loaded month is used, that month is not reported, and Mega Evolutions are ranked as separate entries. Sorting ties and error behavior are left unaddressed but the important ranking semantics are disclosed.

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 sentences, front-loaded with the ranking definition and immediately followed by the usage cue and the caveat. The first sentence is dense with a parenthetical and a trailing 'Mega Evolutions' clause, but no sentence is redundant.

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?

Given an output schema, annotations, and fully documented parameters, the description covers what the agent needs: what is ranked, which month data is used, how Mega Evolutions are treated, and where to go for dated history. Remaining gaps like tie-breaking are minor for this read-only ranking 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?

Schema coverage is 100% and both parameters already document their defaults and limits, so the schema carries most of the burden. The description lightly reinforces that results are scoped to a regulation and to that regulation's most recent month, but adds no new syntax or format details.

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 names a specific verb and resource ('Rank the most-used Pokémon in a regulation') and defines the ranking metric ('share of ladder teams they appear on'), so an agent can distinguish it immediately from get_usage_trend, get_pokemon_usage, and list_formats.

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 gives a concrete use case ('see what a team should expect to face') and explicitly routes dated-history queries to get_usage_trend. It stops short of stating when not to use it or naming other aggregation siblings, but the alternative-for-dates condition is clear.

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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