The Aggregate — LLM benchmark aggregate
Fused LLM rankings: one IRT/Elo scale across ~5,000 public benchmark leaderboards, updated daily.
使うべきか
品質と安全性
ツール定義とプロトコルへの準拠に関する自動分析に基づいています。
コンテキストコスト
これは、サーバーのツールがモデルのコンテキストに読み込まれるたびに消費されるおおよそのトークン数です。数が多いほど、ほかのタスクに使える注意が減ります。
インストール
ワンクリックインストール
これを `claude_desktop_config.json` ファイルに追加してください:
{
"mcpServers": {
"the-aggregate": {
"url": "https://theaggregate.ai/mcp"
}
}
}リモートエンドポイント
https://theaggregate.ai/mcpstreamable-httpできること
ツール一覧
ツール（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.
入力スキーマ
{
"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.
入力スキーマ
{
"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).
入力スキーマ
{
"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.
入力スキーマ
{
"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.
入力スキーマ
{
"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.
入力スキーマ
{
"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.
入力スキーマ
{
"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.
入力スキーマ
{
"type": "object",
"properties": {}
}コミュニティ
エビデンス