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MCP Server

The Aggregate — LLM benchmark aggregate

ai.theaggregate/the-aggregate
AI & Agents Data & Analytics Public & reachable MCP 2026-07-28

What this MCP does

Aggregates and updates LLM benchmark rankings onto a unified IRT/Elo scale.

Capabilities

Tools

8 tools
about_the_aggregate
About The Aggregate
What this data is: how the IRT fusion works, current coverage counts, update cadence, and how to cite it.
Input schema 
{'type': 'object', 'properties': {}}
compare_models
Compare 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.
Input schema 
{'type': 'object', 'required': ['models'], 'properties': {'models': {'type': 'array', 'items': {'type': 'string'}, 'maxItems': 4, 'minItems': 2, 'description': 'Two to four model names or slugs.'}}}
get_benchmark
Benchmark detail
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.
Input schema 
{'type': 'object', 'required': ['benchmark'], 'properties': {'top': {'type': 'number', 'description': 'How many top models to list (1-50, default 10).'}, 'benchmark': {'type': 'string', 'description': 'Benchmark name or slug, e.g. "Aider polyglot".'}}}
get_leaderboard
Aggregate leaderboard
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.
Input schema 
{'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.'}}}
get_model
Model profile
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).
Input schema 
{'type': 'object', 'required': ['model'], 'properties': {'model': {'type': 'string', 'description': 'Model name or slug, e.g. "Claude Opus 4.5" or "gpt-5-5".'}}}
get_prediction_duel
Prediction duel standings
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.
Input schema 
{'type': 'object', 'properties': {}}
search_benchmarks
Search benchmarks
Find benchmarks in the aggregate by (partial) name. Returns model coverage, difficulty on the Elo scale, and the benchmark page URL.
Input schema 
{'type': 'object', 'required': ['query'], 'properties': {'limit': {'type': 'number', 'description': 'Max results (1-25, default 10).'}, 'query': {'type': 'string', 'description': 'Benchmark name fragment, e.g. "swe-bench" or "arena".'}}}
search_models
Search models
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.
Input schema 
{'type': 'object', 'required': ['query'], 'properties': {'limit': {'type': 'number', 'description': 'Max results (1-25, default 10).'}, 'query': {'type': 'string', 'description': 'Model or provider name fragment, e.g. "opus" or "deepseek".'}, '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.'}}}
History

Recent tool changes

Added
about_the_aggregate
Sept. 17, 2026, 7:57 a.m.
Added
get_prediction_duel
Sept. 17, 2026, 7:57 a.m.
Added
get_benchmark
Sept. 17, 2026, 7:57 a.m.
Added
search_benchmarks
Sept. 17, 2026, 7:57 a.m.
Added
compare_models
Sept. 17, 2026, 7:57 a.m.
Added
get_model
Sept. 17, 2026, 7:57 a.m.
Added
search_models
Sept. 17, 2026, 7:57 a.m.
Added
get_leaderboard
Sept. 17, 2026, 7:57 a.m.
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