Ncp vs The Aggregate — LLM benchmark aggregate
A side-by-side look at the Ncp and The Aggregate — LLM benchmark aggregate MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
T
The Aggregate — LLM benchmark aggregateAggregators · Remote HTTP/SSEFused LLM rankings: one IRT/Elo scale across ~5,000 public benchmark leaderboards, updated daily.51GoodAuth: not declaredVerdict: choose Ncp for aggregators work as a local process; choose The Aggregate — LLM benchmark aggregate for aggregators over a hosted endpoint. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Ncp when
- You need dedicated capabilities in the Aggregators domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: No auth required.
- You want tools like
sequentialthinking.
TChoose The Aggregate — LLM benchmark aggregate when
- You need dedicated capabilities in the Aggregators domain.
- You prefer a remote HTTP/SSE transport.
- You want tools like
get_leaderboard, search_models, get_model.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | T The Aggregate — LLM benchmark aggregate | |
|---|---|---|
| Category | Aggregators | Aggregators |
| Quality signal | 69/100 · Great | 51/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Remote HTTP/SSE |
| Auth | No auth required | Not declared |
| Pricing | Free | Not declared |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 100 | — |
| Directory activity | 4 views 0 installs 0 upvotes | 0 views 0 installs 0 upvotes |
| Tools | 1 | 8 |
| Full listing | View Ncp | View The Aggregate — LLM benchmark aggregate |
Tools & capabilities
Ncp tools 1
sequentialthinkingA detailed tool for dynamic and reflective problem-solving through thoughts. This tool helps analyze problems through a flexible thinking process that can adapt and evolve. Each thought can build on, question, or revise previous insights as understanding deepens. When to use this tool: - Breaking down complex problems into steps - Planning and design with room for revision - Analysis that might need course correction - Problems where the full scope might not be clear initially - Problems that require a multi-step solution - Tasks that need to maintain context over multiple steps - Situations where irrelevant information needs to be filtered out Key features: - You can adjust total_thoughts up or down as you progress - You can question or revise previous thoughts - You can add more thoughts even after reaching what seemed like the end - You can express uncertainty and explore alternative approaches - Not every thought needs to build linearly - you can branch or backtrack - Generates a solution hypothesis - Verifies the hypothesis based on the Chain of Thought steps - Repeats the process until satisfied - Provides a correct answer Parameters explained: - thought: Your current thinking step, which can include: * Regular analytical steps * Revisions of previous thoughts * Questions about previous decisions * Realizations about needing more analysis * Changes in approach * Hypothesis generation * Hypothesis verification - nextThoughtNeeded: True if you need more thinking, even if at what seemed like the end - thoughtNumber: Current number in sequence (can go beyond initial total if needed) - totalThoughts: Current estimate of thoughts needed (can be adjusted up/down) - isRevision: A boolean indicating if this thought revises previous thinking - revisesThought: If is_revision is true, which thought number is being reconsidered - branchFromThought: If branching, which thought number is the branching point - branchId: Identifier for the current branch (if any) - needsMoreThoughts: If reaching end but realizing more thoughts needed You should: 1. Start with an initial estimate of needed thoughts, but be ready to adjust 2. Feel free to question or revise previous thoughts 3. Don't hesitate to add more thoughts if needed, even at the "end" 4. Express uncertainty when present 5. Mark thoughts that revise previous thinking or branch into new paths 6. Ignore information that is irrelevant to the current step 7. Generate a solution hypothesis when appropriate 8. Verify the hypothesis based on the Chain of Thought steps 9. Repeat the process until satisfied with the solution 10. Provide a single, ideally correct answer as the final output 11. Only set nextThoughtNeeded to false when truly done and a satisfactory answer is reached
The Aggregate — LLM benchmark aggregate tools 8
get_leaderboardTop 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/offset.search_modelsFind 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.get_modelOne 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).compare_modelsHead-to-head between 2-4 models: aggregate ranks, rating gap with a significance note based on the standard errors, and notable benchmarks they share.search_benchmarksFind benchmarks in the aggregate by (partial) name. Returns model coverage, difficulty on the rating scale, and the benchmark page URL.get_benchmarkOne 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_duelGuesswork — 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_aggregateWhat this data is: how the IRT fusion works, current coverage counts, update cadence, and how to cite it.
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Ncp
{
"mcpServers": {
"portel-dev-ncp": {
"command": "npx",
"args": [
"-y",
"@portel/ncp"
]
}
}
}
The Aggregate — LLM benchmark aggregate
{
"mcpServers": {
"the-aggregate-llm-benchmark-aggregate-2": {
"url": "https://theaggregate.ai/mcp"
}
}
}
Frequently asked questions
Ncp is categorized under Aggregators and uses a local stdio subprocess. The Aggregate — LLM benchmark aggregate belongs to Aggregators and uses a remote HTTP/SSE transport. Pick Ncp for aggregators work and The Aggregate — LLM benchmark aggregate for aggregators.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Ncp is listed as free. Auth: no auth required. The Aggregate — LLM benchmark aggregate has no declared pricing — check the project's own site. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add portel-dev-ncp and the-aggregate-llm-benchmark-aggregate-2 as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Ncp has a quality signal of 69/100 (Great) with 4 views, 0 installs and 100 GitHub stars. The Aggregate — LLM benchmark aggregate has 51/100 (Good) with 0 views, 0 installs and 0 GitHub stars.