Nimbus BCI

Foundation.Compare

foundation.compare

Read-onlyIdempotent

Run every available pretrained backbone on YOUR data and see which wins — a ranked bar chart with CI whiskers, params, and wall time per backbone. Backbones that can't process your data (window size, channel count) are noted, not silently skipped. Each backbone embeds your trials frozen (the exact foundation_features preprocessing), a LDA is cross-validated on the embeddings. Returns a PNG image PLUS a JSON text block (and structuredContent.meta.results) with per-backbone accuracy, CI, embed dim, params, and time — pick the foundation_features backbone by measurement, not vibes.

Input Schema

Name	Required	Description	Default
`mode`	No	Pack split to load: training \| evaluation \| all (source=dataset).	all
`path`	No	Upload path (source=upload) or absolute local path (source=local).
`source`	Yes	"dataset" (MOABB pack), "upload" (path from data.upload), or "local" (absolute path, local backend only).
`dataset`	No	MOABB dataset id, e.g. "BNCI2014_001" (source=dataset).
`subject`	No	Subject id, e.g. "S01" (source=dataset).

Output Schema

Name	Required	Description
`ok`	Yes
`meta`	No	Figure sidecar. foundation.compare includes results/best/skipped (also as a JSON text content block).
`title`	No
`mimeType`	No
`savedPath`	No	Set when save=true.
`sizeBytes`	No	Set when save=true.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 19, 2026

addedOutput schema / properties / meta / description

Added value: +"Figure sidecar. foundation.compare includes results/best/skipped (also as a JSON text content block)."

Changed1 schema field changedOct 16, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "meta": {
+      "additionalProperties": true,
+      "type": "object"
+    },
+    "mimeType": {
+      "type": "string"
+    },
+    "ok": {
+      "type": "boolean"
+    },
+    "savedPath": {
+      "description": "Set when save=true.",
+      "type": "string"
+    },
+    "sizeBytes": {
+      "description": "Set when save=true.",
+      "type": "integer"
+    },
+    "title": {
+      "type": [
+        "string",
+        "null"
+      ]
+    }
+  },
+  "required": [
+    "ok"
+  ],
+  "type": "object"
+}

AddedOct 14, 2026

TDQS

A4.2/5.0

Behavior5/5

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

Annotations cover the safety profile (readOnly, non-destructive, idempotent), and the description goes well beyond them: it discloses failure handling ('noted, not silently skipped'), the exact processing pipeline (frozen embeddings via foundation_features preprocessing, cross-validated LDA), and the full return shape (PNG + JSON block + structuredContent.meta.results). This is unusually rich behavioral context.

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

Conciseness4/5

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

Purpose is front-loaded in the first sentence, followed by edge-case handling, mechanism, and output format — each sentence earns its place. It is slightly dense in the output sentence (listing three return channels) and the 'not vibes' aside is stylistic, but not wasteful.

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

Completeness5/5

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

With a full output schema, 100% param coverage, and annotations present, the remaining burden is explaining what the tool computes and returns — which the description does thoroughly. Nothing an agent needs to call it correctly is missing.

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 description coverage is 100%, so source/path/dataset/subject/mode are already fully documented in the schema. The description adds no parameter-level detail, which is acceptable given the schema does the work — baseline 3.

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

Purpose5/5

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

States a specific verb and resource — run every pretrained backbone on the user's data and rank them — with concrete outputs (bar chart, CI whiskers, params, wall time). It is clearly a benchmarking/comparison tool, distinguishable from execution.run or calibration.train among the siblings.

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?

The closing phrase 'pick the foundation_features backbone by measurement, not vibes' implies the use case (choose a backbone before committing to one), but it never names an alternative tool or states when NOT to run it. Usage is inferable rather than explicit.

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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