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The Aggregate — LLM benchmark aggregate

ai.theaggregate/the-aggregate
KI & Agenten Daten & Analytik Öffentlich und erreichbar MCP 2026-07-28

Was dieses MCP kann

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

Funktionen

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.
Eingabeschema 
{'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.
Eingabeschema 
{'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.
Eingabeschema 
{'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.
Eingabeschema 
{'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).
Eingabeschema 
{'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.
Eingabeschema 
{'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.
Eingabeschema 
{'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.
Eingabeschema 
{'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.'}}}
Verlauf

Letzte Tool-Änderungen

Hinzugefügt
about_the_aggregate
17. September 2026 07:57
Hinzugefügt
get_prediction_duel
17. September 2026 07:57
Hinzugefügt
get_benchmark
17. September 2026 07:57
Hinzugefügt
search_benchmarks
17. September 2026 07:57
Hinzugefügt
compare_models
17. September 2026 07:57
Hinzugefügt
get_model
17. September 2026 07:57
Hinzugefügt
search_models
17. September 2026 07:57
Hinzugefügt
get_leaderboard
17. September 2026 07:57
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