The Aggregate — LLM benchmark aggregate
Fused LLM rankings: one IRT/Elo scale across ~5,000 public benchmark leaderboards, updated daily.
我该使用它吗
质量与安全性
基于对工具定义和协议合规性的自动分析。
上下文开销
这是每次将服务器的工具加载到模型上下文窗口时所消耗的大致 token 数。数值越高，可用于其他任务的注意力就越少。
安装
一键安装
将以下内容添加到你的 `claude_desktop_config.json` 文件中：
{
"mcpServers": {
"the-aggregate": {
"url": "https://theaggregate.ai/mcp"
}
}
}远程端点
https://theaggregate.ai/mcpstreamable-http它能做什么
工具清单
工具（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.
输入模式
{
"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.
输入模式
{
"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).
输入模式
{
"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.
输入模式
{
"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.
输入模式
{
"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.
输入模式
{
"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.
输入模式
{
"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.
输入模式
{
"type": "object",
"properties": {}
}社区
证据