# The Aggregate — LLM benchmark aggregate

**Category:** 💻 Developer Tools  
**Repository:** https://theaggregate.ai/mcp  
**Views:** 0  
**Installs:** 0  
**Upvotes:** 0  
**Directory Page:** https://allmcps.com/mcp/the-aggregate-llm-benchmark-aggregate-2

## Description
Fused LLM rankings: one IRT/Elo scale across ~5,000 public benchmark leaderboards, updated daily.

## Tools
Capabilities this server exposes over MCP:

- **get_leaderboard** — Top of the cross-benchmark aggregate ranking: every model placed on one Elo scale by an IRT model fit over ~5,000 public benchmark leaderboards. Supports paging via limit/offset.
- **search_models** — Find ranked models by (partial) name or provider. Returns rank, Elo and the model page URL.
- **get_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).
- **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.
- **search_benchmarks** — Find benchmarks in the aggregate by (partial) name. Returns model coverage, difficulty on the Elo scale, and the benchmark page URL.
- **get_benchmark** — 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_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.
- **about_the_aggregate** — What this data is: how the IRT fusion works, current coverage counts, update cadence, and how to cite it.

## Claude Desktop Quick Installation
Remote MCP endpoint (confidence: medium). Install path inferred — verify against the README before running it. Add as a URL/SSE server in your client:

```json
"mcpServers": {
  "the-aggregate-llm-benchmark-aggregate": {
    "url": "https://theaggregate.ai/mcp"
  }
}
```

## Documentation
No cached documentation available for this listing yet. Check the repository above for the README and setup instructions.