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SeijiMatsumoto

Valorant MCP Server

get_performance_report

Analyze a Valorant player's recent matches to reveal win rate, combat stats, economy, agent, map, positioning, and weapon patterns for diagnosing performance trends.

Instructions

Broad analysis of a player's recent matches. Start here for "how am I doing / what are my patterns". Sections:

  • overview: win rate (with 95% range), ACS, K/D, ADR, HS%, KAST, opening duels (first kill/death rates, round win % after each), trading (deaths traded, isolated/early deaths), damage conversion, clutches (1v1, 1v2, 1v3+), multikills, and personal stats in wins vs losses

  • sides: attack vs defense round win %, KAST, survival

  • economy: round win % by team buy (pistol/eco/force/full), anti-eco, upsets, and rounds where your own buy didn't match the team's

  • agents / maps: per-role, per-agent and per-map results incl. attack/defense round win %

  • positioning: where you die per map and side (named callouts), first-death zones, isolated %

  • weapons: kills by weapon, median kill distance, K/D by engagement range, results by weapon held

  • context: solo vs party, lobby strength (enemy rank vs yours), game number within a session Numbers are absolute; use get_benchmarks to judge them against same-rank players.

Input Schema

NameRequiredDescriptionDefault
mapNoOnly matches on this map, e.g. 'Ascent'
modeNoQueue to analyzecompetitive
agentNoOnly matches on this agent, e.g. 'Sova'. Fetches extra history to fill `count`.
countNoNumber of recent matches to analyze (max 50)
regionNona, eu, ap, kr, latam or br. Defaults to the server setting.
seasonNoAnalyze a whole act instead of the last `count` games: 'current' (the act of the most recent game) or an act like 'e11a6'. When set, `count` is ignored and up to 100 matches from that act are used.
sectionsNoSubset of sections to return; omit for all
usernameYesRiot ID as name#TAG, e.g. 'player#NA1'

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.2

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses that numbers are absolute (not rank-relative), that the agent filter triggers extra history fetching, and that season overrides count and pulls up to 100 matches. It omits cost/latency expectations and any auth or rate-limit notes, keeping it below a 5.

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?

Purpose and routing are front-loaded in the first two sentences, and the section list is scannable. The enumeration is long but each bullet names a distinct return payload an agent would otherwise have to guess at, so it earns most of its length.

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?

For an 8-parameter, multi-section analytics tool, the description covers what each section returns, how filters interact, and how to interpret the output via get_benchmarks. An output schema exists, so return-value detail beyond the section map is unnecessary, and nothing needed to call it correctly is missing.

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, and the description earns above that by mapping the bullet list directly onto the `sections` enum values and by explaining non-obvious interactions the schema doesn't (agent fetches extra history; season ignores count). It adds no detail on map/mode/region semantics, which the schema already covers.

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?

States a specific verb+resource ('Broad analysis of a player's recent matches') and explicitly positions itself as the entry point ('Start here for "how am I doing"'). The section enumeration makes the scope concrete and distinguishes it from narrower siblings like get_rr_history or get_match_history.

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?

Gives clear usage context ('Start here') and routes the agent to a specific alternative for a specific need: 'use get_benchmarks to judge them against same-rank players.' No explicit when-not guidance versus get_teamplay_report or get_site_report, so it falls short of the 5 bar.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.