The Aggregate — LLM benchmark aggregate
Fused LLM rankings: one IRT/Elo scale across ~5,000 public benchmark leaderboards, updated daily.
사용해야 할까요
품질 및 안전성
도구 정의와 프로토콜 준수에 대한 자동 분석을 기반으로 합니다.
컨텍스트 비용
이는 서버의 도구가 모델의 컨텍스트에 로드될 때마다 소비되는 대략적인 토큰 수입니다. 수치가 높을수록 다른 작업에 사용할 수 있는 주의가 줄어듭니다.
설치
원클릭 설치
`claude_desktop_config.json` 파일에 다음을 추가하세요:
{
"mcpServers": {
"the-aggregate": {
"url": "https://theaggregate.ai/mcp"
}
}
}원격 엔드포인트
https://theaggregate.ai/mcpstreamable-http할 수 있는 일
도구 목록
도구 (8)
🟢get_leaderboard(limit, offset, include_variants)
Top of the cross-benchmark aggregate ranking: every model placed on one Elo scale by an IRT model fit over public benchmark leaderboards (call about_the_aggregate for the current coverage counts). One row per model by default, fused across reasoning-effort settings. Supports paging via limit/offset.
입력 스키마
{
"type": "object",
"properties": {
"limit": {
"type": "number",
"description": "Rows to return (1-100, default 25)."
},
"offset": {
"type": "number",
"description": "Rows to skip from the top (default 0)."
},
"include_variants": {
"type": "boolean",
"description": "Rank each reasoning-effort variant separately (e.g. \"Claude Opus 4.6 (High)\") instead of one fused row per model. Default false."
}
}
}🟢search_models(query, limit, include_variants)
Find ranked models by (partial) name or provider. Returns rank, Elo and the model page URL. One row per model by default, fused across reasoning-effort settings.
입력 스키마
{
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "Model or provider name fragment, e.g. \"opus\" or \"deepseek\"."
},
"limit": {
"type": "number",
"description": "Max results (1-25, default 10)."
},
"include_variants": {
"type": "boolean",
"description": "Return each reasoning-effort variant separately (e.g. \"Claude Opus 4.6 (High)\") instead of one fused row per model. Default false."
}
},
"required": [
"query"
]
}🟢get_model(model)
One model in depth: aggregate rank, Elo with standard error, provider, what it is, cost per task where known, and its most notable benchmark results (with percentiles).
입력 스키마
{
"type": "object",
"properties": {
"model": {
"type": "string",
"description": "Model name or slug, e.g. \"Claude Opus 4.5\" or \"gpt-5-5\"."
}
},
"required": [
"model"
]
}⚪compare_models(models)
Head-to-head between 2-4 models: aggregate ranks, Elo gap with a significance note based on the standard errors, and notable benchmarks they share.
입력 스키마
{
"type": "object",
"properties": {
"models": {
"type": "array",
"items": {
"type": "string"
},
"minItems": 2,
"maxItems": 4,
"description": "Two to four model names or slugs."
}
},
"required": [
"models"
]
}🟢search_benchmarks(query, limit)
Find benchmarks in the aggregate by (partial) name. Returns model coverage, difficulty on the Elo scale, and the benchmark page URL.
입력 스키마
{
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "Benchmark name fragment, e.g. \"swe-bench\" or \"arena\"."
},
"limit": {
"type": "number",
"description": "Max results (1-25, default 10)."
}
},
"required": [
"query"
]
}🟢get_benchmark(benchmark, top)
One benchmark in depth: what it measures, the original source leaderboard URL, IRT stats (difficulty, noise, model coverage), skill weights, and the current top models on it.
입력 스키마
{
"type": "object",
"properties": {
"benchmark": {
"type": "string",
"description": "Benchmark name or slug, e.g. \"Aider polyglot\"."
},
"top": {
"type": "number",
"description": "How many top models to list (1-50, default 10)."
}
},
"required": [
"benchmark"
]
}🟢get_prediction_duel
Guesswork — the public prediction duel: every day frontier LLMs and The Aggregate's own IRT model predict newly scraped benchmark scores before seeing them, and the errors are scored. Returns the current monthly standings, wins and losses included.
입력 스키마
{
"type": "object",
"properties": {}
}🟡about_the_aggregate
What this data is: how the IRT fusion works, current coverage counts, update cadence, and how to cite it.
입력 스키마
{
"type": "object",
"properties": {}
}커뮤니티
증거