Nimbus BCI

Catalog.Leaderboard

catalog.leaderboard

Read-onlyIdempotent

Public benchmark leaderboard: pipeline rankings per dataset.

Two tracks: the top-level within_session block (curated suite pipelines) and crossSubject (leave-one-subject-out — curated pipelines plus BYO python_model submissions; BYO rows carry kind: "byo" and an owner name). Within each dataset, rows sort desc by meanAccuracyPct (95% CI in ciLoPct/ciHiPct). packFingerprint identifies the exact dataset pack behind the scores; updated marks each dataset's most recent run. The pretrainedFewTrial efficiency track means: accuracy at a protocol-fixed 100-trials-per-subject budget — the frozen-backbone / few-parameter rows that matter when calibration data is scarce (paramsTrained shown per row so cost is readable next to accuracy). submittableDatasets is the set open for leaderboard.submit; a dataset can appear under crossSubject and still be closed for new submissions.

Input Schema

Name	Required	Description	Default
`dataset_id`	No	Optional dataset id. When set, every track is filtered to that dataset. Omit it for the full board.

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 18, 2026

addedInput schema / properties / dataset_id

Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Optional dataset id. When set, every track is filtered\nto that dataset. Omit it for the full board."
+}

AddedOct 5, 2026

TDQS

B3.4/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, idempotent, openWorld, and non-destructive, so the safety profile is fully covered. The description adds substantial domain context (tracks, BYO rows, the pretrainedFewTrial efficiency track), but none of it is behavioral — no auth, rate-limit, or side-effect information beyond what the hints provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

It is front-loaded with the purpose, which is good, but then spends most of its length documenting return-value fields (rows, packFingerprint, updated, ciLoPct/ciHiPct) that the output schema already carries. Several sentences could be trimmed without losing anything an agent needs to invoke the tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present, the description needn't explain return values yet does so thoroughly, so the calling context is more than complete. The only gap is guidance on choosing this tool over its leaderboard siblings, which leaves the picture slightly incomplete for tool selection.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the single optional dataset_id is fully documented in-schema ('When set, every track is filtered to that dataset. Omit it for the full board.'). The description mentions datasets repeatedly but adds no filtering semantics beyond the schema, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening sentence gives a specific verb and resource: 'Public benchmark leaderboard: pipeline rankings per dataset.' It implicitly distinguishes itself from leaderboard.submit by noting that submittableDatasets are 'open for leaderboard.submit,' but it never contrasts with siblings like leaderboard.mine or leaderboard.status, so an agent still has to infer which leaderboard tool to pick.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied by the resource name — the description never says when to call this versus leaderboard.mine, leaderboard.status, or plots.leaderboard. The one useful routing hint is that submittableDatasets marks what leaderboard.submit accepts, but there are no exclusions or prerequisites stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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