Nimbus BCI
AI agents build, train, and analyze BCI/EEG pipelines: data, models, experiments, live sessions.
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Calidad y seguridad
Hallazgos (38)
- LOWTool 'catalog.node_schema' description lacks action verben catalog.node_schema
- LOWTool 'account.whoami' doesn't follow camelCase/snake_caseen account.whoami
- LOWTool 'catalog.nodes' doesn't follow camelCase/snake_caseen catalog.nodes
- LOWTool 'catalog.node_schema' doesn't follow camelCase/snake_caseen catalog.node_schema
- LOWTool 'catalog.templates' doesn't follow camelCase/snake_caseen catalog.templates
- LOWTool 'catalog.template' doesn't follow camelCase/snake_caseen catalog.template
- LOWTool 'catalog.datasets' doesn't follow camelCase/snake_caseen catalog.datasets
- LOWTool 'catalog.leaderboard' doesn't follow camelCase/snake_caseen catalog.leaderboard
- LOWTool 'pipeline.validate' doesn't follow camelCase/snake_caseen pipeline.validate
- LOWTool 'pipeline.validate_node' doesn't follow camelCase/snake_caseen pipeline.validate_node
Basado en el análisis automatizado de las definiciones de herramientas y el cumplimiento del protocolo.
Costo de contexto
Este es el número aproximado de tokens que se consumen cada vez que las herramientas del servidor se cargan en el contexto de un modelo. Los recuentos más altos reducen la atención disponible para otras tareas.
Instalar
Instalación con un clic
Agrega esto a tu archivo `claude_desktop_config.json`:
{
"mcpServers": {
"nimbus-mcp": {
"command": "uvx",
"args": [
"nimbus-mcp"
]
}
}
}Paquetes ejecutables
0.8.1stdioPuntos de conexión remotos
https://nimbus-mcp.fly.dev/mcpstreamable-httpQué puede hacer
Inventario de herramientas
Herramientas (37)
🟢account.whoami
Who you are authenticated as: account email, plan (isPro / pioneer), this month's free-run quota, and — with a hosted token — the token name and days until it expires. Call this first when setup guidance appears or to check which credential a session uses.
Esquema de entrada
{
"type": "object",
"properties": {},
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟢catalog.nodes(category)
List Nimbus pipeline node types (data, preprocessing, features, models...). Use catalog.node_schema(node_type) for one node's full config schema and ports.
Esquema de entrada
{
"type": "object",
"properties": {
"category": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional filter — e.g. \"data\", \"preprocessing\", \"features\",\n\"models\" (exact category ids from the unfiltered list)."
}
},
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟢catalog.node_schema(node_type)
Full config JSON schema + input/output ports for one node type.
Esquema de entrada
{
"type": "object",
"properties": {
"node_type": {
"type": "string",
"description": "Node id from catalog.nodes (e.g. \"csp\", \"nimbus_lda\")."
}
},
"required": [
"node_type"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟢catalog.templates
List built-in starter pipelines (MI/P300/SSVEP...). catalog.template(id) returns the graph.
Esquema de entrada
{
"type": "object",
"properties": {},
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟢catalog.template(template_id)
Full template incl. the 'train' execGraph needed by execution.run/pipeline.validate.
Esquema de entrada
{
"type": "object",
"properties": {
"template_id": {
"type": "string",
"description": "Template id from catalog.templates (e.g. \"mi_csp_lda\")."
}
},
"required": [
"template_id"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟢catalog.datasets(only_on_disk)
Curated public EEG datasets (MOABB packs) available to pipelines.
Esquema de entrada
{
"type": "object",
"properties": {
"only_on_disk": {
"default": true,
"type": "boolean",
"description": "Only return datasets whose data packs are present on this\nbackend (True by default; False also lists known-but-missing sets)."
}
},
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟢catalog.leaderboard
Public benchmark leaderboard: pipeline rankings per dataset. Rankings are per-dataset under the canonical ``within_session`` protocol (see ``protocol``). Within each dataset, ``rows`` are sorted desc by ``meanAccuracyPct`` (95% CI in ``ciLoPct``/``ciHiPct``). Use ``pipelineId`` as the template id hint for ``catalog.template`` when building a pipeline. ``updated`` marks each dataset's most recent run; ``packFingerprint`` identifies the exact dataset pack the scores came from.
Esquema de entrada
{
"type": "object",
"properties": {},
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟢pipeline.validate(train_graph)
Validate a pipeline graph before running. ExecGraphSnapshot: {nodes: [{id, type, config}], connections: [{from, to}]}. Build it from catalog.template(id).train or from scratch using catalog.nodes().
Esquema de entrada
{
"type": "object",
"properties": {
"train_graph": {
"additionalProperties": true,
"type": "object",
"description": "The graph to validate ({nodes, connections})."
}
},
"required": [
"train_graph"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟢pipeline.validate_node(node_type, config)
Validate one node's config object against its schema (catalog.node_schema).
Esquema de entrada
{
"type": "object",
"properties": {
"node_type": {
"type": "string",
"description": "Node type id from catalog.nodes (e.g. \"csp\", \"nimbus_lda\")."
},
"config": {
"additionalProperties": true,
"type": "object",
"description": "The node's config object to check."
}
},
"required": [
"node_type",
"config"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}⚪execution.run(train_graph, name, description, subject, layout)
Start a pipeline run (NON-BLOCKING). Returns executionId — poll with execution.get() until status is completed/failed, then execution.results().
Esquema de entrada
{
"type": "object",
"properties": {
"train_graph": {
"additionalProperties": true,
"type": "object",
"description": "Pipeline graph {nodes: [{id, type, config}], connections:\n[{from, to}]} as built by catalog.template/pipeline.validate."
},
"name": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Display name for the run (shown in the Runs list)."
},
"description": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional longer description of the experiment."
},
"subject": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional dataset subject code (e.g. \"S01\") recorded with the run."
},
"layout": {
"anyOf": [
{
"additionalProperties": true,
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional canvas positions {nodes: {id: {x, y}}}; a deterministic\ngrid is synthesized when omitted."
}
},
"required": [
"train_graph"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🔴execution.cancel(execution_id)
Cancel a running execution.
Esquema de entrada
{
"type": "object",
"properties": {
"execution_id": {
"type": "string",
"description": "The run to terminate (from execution.run/execution.list)."
}
},
"required": [
"execution_id"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟢execution.get(execution_id)
Execution status summary (status: running/completed/failed/cancelled).
Esquema de entrada
{
"type": "object",
"properties": {
"execution_id": {
"type": "string",
"description": "The run to check (from execution.run/execution.list)."
}
},
"required": [
"execution_id"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟢execution.list(limit, status)
Recent executions. Optional status filter (running/completed/failed/cancelled).
Esquema de entrada
{
"type": "object",
"properties": {
"limit": {
"default": 20,
"type": "integer",
"description": "Maximum number of runs to return (default 20)."
},
"status": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Filter by run status: running/completed/failed/cancelled."
}
},
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟢execution.results(execution_id, full)
Metrics for a completed run. Trimmed by default (accuracy, kappa, ITR, confusion matrix, per-class); full=True returns the complete result object.
Esquema de entrada
{
"type": "object",
"properties": {
"execution_id": {
"type": "string",
"description": "The completed run to fetch metrics for."
},
"full": {
"default": false,
"type": "boolean",
"description": "Return the backend's complete result object (all fields)."
}
},
"required": [
"execution_id"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟢calibration.start(paradigm, confirm, trials_per_class, classes, name, ...)
Start a guided subject calibration session (NON-BLOCKING; confirm-gated — the device goes on a human's head). The Nimbus Studio app shows the cues on its calibration dashboard automatically; poll calibration.status. Requires a Pro plan (hosted token or Pro session): calibration nodes and custom-data training are gated by the freemium node policy; local X-MCP-Key principals get 403 by policy.
Esquema de entrada
{
"type": "object",
"properties": {
"paradigm": {
"default": "mi",
"type": "string",
"description": "mi | p300 | sart | target_hit."
},
"confirm": {
"default": false,
"type": "boolean",
"description": "MUST be true — explicit user go-ahead for a session on their head."
},
"trials_per_class": {
"anyOf": [
{
"type": "integer"
},
{
"type": "null"
}
],
"default": null,
"description": "Override the template's trial count (minimum 5)."
},
"classes": {
"anyOf": [
{
"items": {
"additionalProperties": true,
"type": "object"
},
"type": "array"
},
{
"type": "null"
}
],
"default": null,
"description": "Override class list [{id,label,cue}] (MI default: left/right hand)."
},
"name": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Display name recorded on the execution."
},
"device_type": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Device id from device.list (omit → template default synthetic)."
},
"connection_type": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Device selector when several exist (e.g. serial vs wifi)."
},
"port": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Serial/COM port for wired devices."
},
"ip_address": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Device IP for network/wifi devices."
},
"ip_port": {
"anyOf": [
{
"type": "integer"
},
{
"type": "null"
}
],
"default": null,
"description": "Port for network devices."
},
"stream_name": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "LSL stream name (LSL devices)."
},
"source_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "LSL source id."
},
"mac_address": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Bluetooth MAC (BT devices)."
},
"serial_number": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Device serial (some BLE stacks)."
}
},
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟢calibration.status(execution_id)
Live snapshot of a calibration session (phase, current trial, progress, paused). Once complete, carries the recorded upload — call calibration.train to turn it into the subject's own classifier.
Esquema de entrada
{
"type": "object",
"properties": {
"execution_id": {
"type": "string",
"description": "The calibration run to inspect (from calibration.start)."
}
},
"required": [
"execution_id"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}⚪calibration.pause(execution_id)
Pause a running calibration between trials (cues hold; resume anytime).
Esquema de entrada
{
"type": "object",
"properties": {
"execution_id": {
"type": "string",
"description": "The calibration run to pause."
}
},
"required": [
"execution_id"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}⚪calibration.resume(execution_id)
Resume a paused calibration session.
Esquema de entrada
{
"type": "object",
"properties": {
"execution_id": {
"type": "string",
"description": "The calibration run to resume."
}
},
"required": [
"execution_id"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟡calibration.train(execution_id, paradigm, template_id, train_graph, name)
Train the subject's own classifier from a COMPLETED calibration session (NON-BLOCKING). Fetches the recorded upload, wires it into a train pipeline as a custom_data source, and starts the run. Requires a Pro plan (custom_data training is freemium-gated). The calibrate→train handoff requires a Postgres-backed backend (hosted or local dev); a desktop-local session completes and records, but its upload can't be resolved by MCP train today.
Esquema de entrada
{
"type": "object",
"properties": {
"execution_id": {
"type": "string",
"description": "The COMPLETED calibration run (from calibration.start)."
},
"paradigm": {
"default": "mi",
"type": "string",
"description": "Paradigm of the recording (mi | p300 | sart | target_hit);\nonly mi has a default train template."
},
"template_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Train template to use (e.g. from catalog.templates);\nrequired for non-mi paradigms (mi defaults to mi_headband_csp_lda)."
},
"train_graph": {
"anyOf": [
{
"additionalProperties": true,
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"description": "Explicit train graph instead of a template; its first\ndata node (custom_data/public_data) is rewired onto the recording."
},
"name": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Display name recorded on the training run."
}
},
"required": [
"execution_id"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}⚪experiment.run(runs, max_concurrent)
Run 1-25 pipelines as ONE paced experiment (NON-BLOCKING). Returns an experimentId immediately; a background thread submits at most 2 runs at a time (min(max_concurrent, 2)), retries queue-full up to 3 times per run, and polls each execution to completion. Poll experiment.get() for per-run status and, once finished, aggregated metrics.
Esquema de entrada
{
"type": "object",
"properties": {
"runs": {
"items": {
"additionalProperties": true,
"type": "object"
},
"type": "array",
"description": "1-25 entries, each {name: str, train_graph: {nodes, connections}}\n(same graph shape as execution.run's train_graph)."
},
"max_concurrent": {
"default": 2,
"type": "integer",
"description": "Parallel submissions cap, clamped to 1-2 (default 2)."
}
},
"required": [
"runs"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟢experiment.get(experiment_id)
Experiment snapshot: status (running/completed/failed), per-run rows ({name, executionId, status, error?, metrics?}) and, once finished, aggregates {metric: {mean, std, best: {name, value}}} over completed runs only (std = population; None below 2 values).
Esquema de entrada
{
"type": "object",
"properties": {
"experiment_id": {
"type": "string",
"description": "The experiment to inspect (from experiment.run)."
}
},
"required": [
"experiment_id"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟢execution.artifacts(execution_id)
Trained artifacts (models/filters, e.g. *.pkl) saved by an execution.
Esquema de entrada
{
"type": "object",
"properties": {
"execution_id": {
"type": "string",
"description": "Run whose artifacts to list."
}
},
"required": [
"execution_id"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟢execution.download_artifact(execution_id, artifact_name)
Download one artifact file to NIMBUS_EXPORT_DIR/executions/<id>/ and return its path.
Esquema de entrada
{
"type": "object",
"properties": {
"execution_id": {
"type": "string",
"description": "Run that produced the artifact."
},
"artifact_name": {
"type": "string",
"description": "File name from execution.artifacts (e.g. \"nimbus_lda.pkl\")."
}
},
"required": [
"execution_id",
"artifact_name"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟢pipeline.export(train_graph, name)
Export the pipeline as a standalone runnable Python bundle (zip saved locally).
Esquema de entrada
{
"type": "object",
"properties": {
"train_graph": {
"additionalProperties": true,
"type": "object",
"description": "Pipeline graph {nodes, connections} to export."
},
"name": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional name recorded inside the bundle."
}
},
"required": [
"train_graph"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟢device.list
EEG devices supported by this backend (OpenBCI, Muse, BrainBit, LSL, PiEEG...).
Esquema de entrada
{
"type": "object",
"properties": {},
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟢device.test(device_type, connection_type, port, ip_address, ip_port, ...)
Test a device connection WITHOUT starting a stream (safe, no confirm needed).
Esquema de entrada
{
"type": "object",
"properties": {
"device_type": {
"type": "string",
"description": "Device id from device.list (e.g. \"brainbit\", \"muse\")."
},
"connection_type": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Device-specific selector when several exist (e.g. serial vs wifi)."
},
"port": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Serial/COM port for wired devices."
},
"ip_address": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Device IP for network/wifi devices."
},
"ip_port": {
"anyOf": [
{
"type": "integer"
},
{
"type": "null"
}
],
"default": null,
"description": "Port for network devices."
},
"stream_name": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "LSL stream name (LSL devices)."
},
"source_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "LSL source id."
},
"mac_address": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Bluetooth MAC (BT devices)."
},
"serial_number": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Device serial (some BLE stacks)."
}
},
"required": [
"device_type"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}⚪stream.start(device_type, confirm, session_id, chunk_size, n_channels, ...)
Connect an EEG device and START a live streaming session on the user's head. Requires confirm=True; call device.test first. Track with stream.status(). Idle watchdog: if no stream.status()/stream.telemetry() poll happens for idle_timeout_sec (default 900), the session is stopped and the device disconnected automatically — an abandoned stream never keeps running on the user's head. Any poll resets the timer; idle_timeout_sec=0 disables the watchdog.
Esquema de entrada
{
"type": "object",
"properties": {
"device_type": {
"type": "string",
"description": "Device id from device.list (e.g. \"brainbit\")."
},
"confirm": {
"default": false,
"type": "boolean",
"description": "MUST be true to start — the explicit user go-ahead for a live\nsession on their head; anything else is refused with zero requests."
},
"session_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional existing session to resume/reuse."
},
"chunk_size": {
"default": 125,
"type": "integer",
"description": "Samples per streamed chunk (default 125)."
},
"n_channels": {
"default": 8,
"type": "integer",
"description": "Channel count to open (default 8)."
},
"connection_type": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Device-specific selector (e.g. serial vs wifi)."
},
"port": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Serial/COM port for wired devices."
},
"ip_address": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Device IP for network/wifi devices."
},
"ip_port": {
"anyOf": [
{
"type": "integer"
},
{
"type": "null"
}
],
"default": null,
"description": "Port for network devices."
},
"stream_name": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "LSL stream name (LSL devices)."
},
"source_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "LSL source id."
},
"mac_address": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Bluetooth MAC (BT devices)."
},
"serial_number": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Device serial (some BLE stacks)."
},
"idle_timeout_sec": {
"default": 900,
"type": "integer",
"description": "Watchdog: stop+disconnect after this many seconds\nwithout a status poll (default 900; 0 disables)."
}
},
"required": [
"device_type"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟢stream.status(session_id)
Live snapshot of a streaming session (running, deviceConnected). Polling this also feeds the idle watchdog: each call resets the session's idle timer (see stream.start's idle_timeout_sec).
Esquema de entrada
{
"type": "object",
"properties": {
"session_id": {
"type": "string",
"description": "The streaming session to inspect."
}
},
"required": [
"session_id"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🔴stream.stop(session_id)
Stop a streaming session and disconnect the device (always safe to call). Also removes the session from the idle watchdog so it cannot fire after an explicit stop.
Esquema de entrada
{
"type": "object",
"properties": {
"session_id": {
"type": "string",
"description": "The streaming session to stop."
}
},
"required": [
"session_id"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟢stream.telemetry(session_id, window)
Live snapshot of a streaming session: latest prediction + recent window, signal quality (meanChannelQuality, snrDb, artifactProbability), indicators, running stats. Poll this while a session runs. Live telemetry requires a DEPLOYED model session (hub deploy / playback with a classifier); modelless hardware streams have no telemetry — use stream.status for those. Expect low confidence during filter/ASR warm-up (first seconds); quality < 0.5 or high artifactProbability means the signal is poor. 404 => session not active in this backend. Each poll also feeds the idle watchdog (see stream.start's idle_timeout_sec), keeping an actively watched session alive.
Esquema de entrada
{
"type": "object",
"properties": {
"session_id": {
"type": "string",
"description": "The streaming session to read telemetry for."
},
"window": {
"default": 50,
"type": "integer",
"description": "How many recent predictions/chunks to include (default 50)."
}
},
"required": [
"session_id"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟡data.upload(file_path, dataset_name, sampling_rate, format)
Upload an EEG file (.edf/.bdf/.mat/.csv/.txt/.tsv/.h5/.hdf5, <=500MB) to the backend and get the registered path for a custom_data node. sampling_rate (Hz, e.g. 250.0) is REQUIRED for plain CSV/TSV/TXT files without embedded metadata — the backend silently assumes 250 Hz otherwise, which mis-times epochs, filters and spectral features. format overrides extension-based detection (auto, mat, csv, tsv, txt, edf, bdf, h5, hdf5).
Esquema de entrada
{
"type": "object",
"properties": {
"file_path": {
"type": "string",
"description": "Local file to upload (.edf/.bdf/.mat/.csv/.txt/.tsv/.h5/.hdf5, <=500MB)."
},
"dataset_name": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional label for the uploaded dataset."
},
"sampling_rate": {
"anyOf": [
{
"type": "number"
},
{
"type": "null"
}
],
"default": null,
"description": "Hz for headerless CSV/TSV/TXT (REQUIRED there, e.g. 250.0)."
},
"format": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Override extension-based detection (auto|mat|csv|tsv|txt|edf|bdf|h5|hdf5)."
}
},
"required": [
"file_path"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟢data.inspect_dataset(dataset, subject, mode)
Exploratory summary of a public EEG dataset (MOABB pack): channels, sampling rate, trial/class balance, per-channel µV stats, band powers and a PSD overview. Look at the data BEFORE building pipelines: class balance drives stratification choices (imbalanced classes skew accuracy), and flatlined channels mean a montage/reference problem worth fixing first. subject is REQUIRED (the backend 400s without it) — get the subject list via catalog.datasets, e.g. "S01"; a comma-list like "S01,S03" loads a cohort. mode: training | evaluation | all. Units note: values are ASSUMED volts by the loader — a µV-native file reads 1e6x too large; set unitsScale in a pipeline's custom_data config when needed.
Esquema de entrada
{
"type": "object",
"properties": {
"dataset": {
"type": "string",
"description": "Dataset id from catalog.datasets (e.g. \"BNCI2014_001\")."
},
"subject": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "REQUIRED subject code (\"S01\") or comma-list cohort (\"S01,S03\")."
},
"mode": {
"default": "all",
"type": "string",
"description": "Which split to summarize — training | evaluation | all."
}
},
"required": [
"dataset"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟢data.inspect_file(path)
Exploratory summary of an EEG file (.edf/.bdf/.mat/.csv/.tsv/.txt/.h5): channels, sampling rate, trial/class balance, per-channel µV stats, band powers and a PSD overview. The path shape picks the source: ABSOLUTE path → read the file from disk (only on a LOCAL backend: desktop app / MCP local mode — no upload needed); RELATIVE path (the one data.upload returns) → describe the uploaded file, which works on ANY backend (hosted or local). Look at the data BEFORE building pipelines: class balance drives stratification choices, and flatlined channels mean a montage/reference problem worth fixing first. Units note: values are ASSUMED volts by the loader — a µV-native CSV reads 1e6x too large; set unitsScale in a pipeline's custom_data config when needed. On a hosted backend absolute paths are refused and this returns guidance (upload the file first or switch to a local backend).
Esquema de entrada
{
"type": "object",
"properties": {
"path": {
"type": "string",
"description": "ABSOLUTE filesystem path (local backends only) or the RELATIVE\nupload path returned by data.upload (works on any backend)."
}
},
"required": [
"path"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟡project.create(name, description)
Create a project (container for one pipeline document). Returns projectId.
Esquema de entrada
{
"type": "object",
"properties": {
"name": {
"type": "string",
"description": "Project display name (e.g. \"MI CSP-LDA sweep\")."
},
"description": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional longer description shown in the studio."
}
},
"required": [
"name"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟢project.list
List projects owned by the current principal (agent work included).
Esquema de entrada
{
"type": "object",
"properties": {},
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟡project.save(project_id, train_graph, name, subject)
Save a pipeline graph into a project (visible on the studio canvas). Handles revision conflicts automatically (one retry).
Esquema de entrada
{
"type": "object",
"properties": {
"project_id": {
"type": "string",
"description": "Target project (from project.create/project.list)."
},
"train_graph": {
"additionalProperties": true,
"type": "object",
"description": "Pipeline graph {nodes, connections} to persist."
},
"name": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional pipeline name stored on the document."
},
"subject": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional dataset subject code (e.g. \"S01\") for this pipeline."
}
},
"required": [
"project_id",
"train_graph"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}🟢project.load(project_id)
Load a project's saved pipeline (train graph + meta) for editing/re-running.
Esquema de entrada
{
"type": "object",
"properties": {
"project_id": {
"type": "string",
"description": "Project whose pipeline document to load."
}
},
"required": [
"project_id"
],
"additionalProperties": false
}Esquema de salida
{
"type": "object",
"additionalProperties": true
}Comunidad
Evidencia