MCP server

The Aggregate — LLM benchmark aggregate Official

ai.theaggregate/the-aggregate

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

LLM rankings from public benchmarks, model comparisons and source-linked results. Updated daily.

Website

At a glance

Type

MCP server

Publisher

theaggregate.ai (verified by the MCP Registry)

Status

Working at last check

Tools

8

Token cost

About 1,035 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http the-aggregate https://theaggregate.ai/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.the-aggregate]
url = "https://theaggregate.ai/mcp"

Gemini CLI

{"mcpServers":{"the-aggregate":{"httpUrl":"https://theaggregate.ai/mcp"}}}

Cursor

{"mcpServers":{"the-aggregate":{"url":"https://theaggregate.ai/mcp"}}}

VS Code

{"servers":{"the-aggregate":{"type":"http","url":"https://theaggregate.ai/mcp"}}}

Antigravity

agy mcp add the-aggregate https://theaggregate.ai/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"the-aggregate","server_url":"https://theaggregate.ai/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"the-aggregate","server_url":"https://theaggregate.ai/mcp"}

Tools

• about_the_aggregate ~58 tokens
  What this data is: how the IRT fusion works, current coverage counts, update cadence, and how to cite it.
• compare_models ~112 tokens
  Head-to-head between 2-4 models: aggregate ranks, rating gap with a significance note based on the standard errors, and notable benchmarks they share.
• get_benchmark ~135 tokens
  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.
• get_leaderboard ~212 tokens
  Top of the cross-benchmark aggregate ranking: every model placed on one rating 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/offs
• get_model ~112 tokens
  One model in depth: aggregate rank, Aggregate Rating with standard error, provider, what it is, cost per task where known, and its most notable benchmark results (with percentiles).
• get_prediction_duel ~99 tokens
  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.
• search_benchmarks ~121 tokens
  Find benchmarks in the aggregate by (partial) name. Returns model coverage, difficulty on the rating scale, and the benchmark page URL.
• search_models ~186 tokens
  Find ranked models by (partial) name or provider. Returns rank, Aggregate Rating and the model page URL. One row per model by default, fused across reasoning-effort settings.

Browse

• Calling other AI models

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