BCI/EEG toolkit: public MOABB/BNCI datasets, pipeline build/validate, hosted training, BIDS export.
Connect Remote server
claude mcp add --transport http nimbus-mcp https://nimbus-mcp.fly.dev/mcp{
"mcpServers": {
"nimbus-mcp": {
"url": "https://nimbus-mcp.fly.dev/mcp"
}
}
}{
"servers": {
"nimbus-mcp": {
"type": "http",
"url": "https://nimbus-mcp.fly.dev/mcp"
}
}
}Settings > Connectors > Add custom connector
URL: https://nimbus-mcp.fly.dev/mcpTools 63 found in our last check
account.whoamiWho 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. In anonymous Local Fre…
catalog.nodesList Nimbus pipeline node types (data, preprocessing, features, models...). Use catalog.node_schema(node_type) for one node's full config schema and ports.
catalog.node_schemaFull config JSON schema + input/output ports for one node type.
catalog.templatesList built-in starter pipelines (MI/P300/SSVEP...). catalog.template(id) returns the graph. Each row carries ``datasetId`` (catalog id, e.g. "BNCI2014_001" — the id catalog.leaderboard and catalog.datasets use) and ``paradigm`` when the template loads a public dataset.
catalog.templateFull template incl. the 'train' execGraph needed by execution.run/pipeline.validate.
catalog.datasetsCurated public EEG datasets (MOABB packs) available to pipelines.
catalog.leaderboardPublic 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…
leaderboard.submitSubmit a BYO python_model to a leaderboard track for server-side scoring. Nimbus scores it server-side under the track's fixed protocol — you submit code, never results. Poll ``leaderboard.status`` with the returned ``submissionId``; scoring takes minutes (classical) to tens of…
leaderboard.statusFetch one of your leaderboard submissions (owner-only). Returns status (queued | scoring | done | failed), metrics when done (meanAccuracyPct + CI, same shape as leaderboard rows), or the error that failed it.
leaderboard.mineList your leaderboard submissions (newest first), all datasets.
pipeline.validateValidate 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().
pipeline.validate_nodeValidate one node's config (schema + semantic checks). Catches the same value errors as pipeline.validate for a single node (inverted bandpass, CSP components vs channel count when dataset is set).
execution.runStart a pipeline run (NON-BLOCKING). Returns executionId — poll with execution.get() until status is completed/failed, then execution.results(). If an identical graph is already running for you, the run still starts but the response carries a ``warning`` + ``duplicateOf`` ids (c…
execution.cancelCancel a running execution.
execution.deletePermanently delete one of your finished runs (completed/failed/cancelled) with its results. Running runs must be cancelled first (409 nimbus.execution.delete_not_terminal). Owner only.
execution.getExecution status summary (status: running/completed/failed/cancelled). Summary fields: ``dataset`` is the human-readable display label (e.g. "BCIIV2a"); ``datasetId`` is the catalog id (e.g. "BNCI2014_001") accepted by other tools (data.inspect_dataset, public_data.dataset). ``t…
execution.listRecent executions. Optional status filter (running/completed/failed/cancelled). Per-run fields: ``dataset`` is the human-readable display label (e.g. "BCIIV2a"); ``datasetId`` is the catalog id (e.g. "BNCI2014_001") accepted by other tools (data.inspect_dataset, public_data.data…
execution.resultsMetrics for a completed run. Trimmed by default (accuracy, kappa, ITR, confusion matrix, per-class); full=True returns the complete result object.
calibration.startStart 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…
calibration.statusLive 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.
calibration.pausePause a running calibration between trials (cues hold; resume anytime).
calibration.resumeResume a paused calibration session.
calibration.trainTrain 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 hand…
experiment.runRun 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()…
experiment.deleteDelete one of your experiment records (server + this machine's local cache). Its individual runs are kept — delete them with execution.delete. A still-running experiment cannot be deleted.
experiment.getExperiment snapshot: status (running/completed/failed), per-run rows ({name, executionId, status, error?, metrics?, evaluationKind?}) and, once finished, aggregates over completed runs only: per-metric {mean, std, best, n}, plus ``pooledExecutionIds`` / ``pooledRunNames`` / ``ki…
execution.artifactsTrained artifacts (models/filters, e.g. *.pkl) saved by an execution.
execution.download_artifactDownload one artifact file. Local/stdio: saved to NIMBUS_EXPORT_DIR/executions/<id>/ and its path returned. Hosted gateway: returned inline as ``contentBase64`` (up to 1 MB of base64); larger files point to Nimbus Studio. Nothing is written on the gateway.
pipeline.exportExport the pipeline as a standalone runnable Python bundle (zip). Local/stdio: the zip is saved under NIMBUS_EXPORT_DIR/exports and its path returned. Hosted gateway: the zip is returned inline as ``contentBase64`` (up to 1 MB of base64). UI-only nodes (interactive_application)…
bids.export_datasetExport a Nimbus dataset as a BIDS-layout zip (local: saved to NIMBUS_EXPORT_DIR/bids/; hosted gateway: inline ``contentBase64``, up to 1 MB). Continuous sources (EDF/BDF uploads, stream recordings) export as BIDS-raw (EDF byte-passthrough; recordings converted HDF5→EDF with even…
bids.export_executionExport an execution's results as a BIDS-derivative zip (metrics.json, per-subject participants.tsv, protocol.json, full pipeline_snapshot.json, predictions.tsv when stored, provenance/execution.json). Local: saved to NIMBUS_EXPORT_DIR/bids/. Hosted gateway: inline ``contentBase6…
device.listEEG devices supported by this backend (OpenBCI, Muse, BrainBit, LSL, PiEEG...).
device.testTest a device connection WITHOUT starting a stream (safe, no confirm needed).
stream.startConnect 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…
stream.statusLive 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).
stream.stopStop 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.
plots.confusionConfusion-matrix figure for one execution: heatmap with trial counts + per-class accuracy bars, rendered as a PNG the assistant can see. Use it right after execution.results to eyeball WHICH classes are confused, not just the accuracy number.
plots.datasetFigure over an EEG data source — the visual companion to data.inspect_dataset. kind=psd (Welch, channel mean bold), kind=bandpower (channel × band dB heatmap), kind=topomap (band-power scalp map), kind=spectrogram (time-frequency for one channel). Sources are the same ones inspe…
plots.erpERP butterfly figure from epoched data: one subplot per class, all channel means (thin) + across-channel mean (bold), time-locked to the trial event. Shows whether class-conditioned evoked structure exists BEFORE training anything.
plots.leaderboardLeaderboard bar chart for one dataset: accuracy with 95% CI whiskers, best first; BYO python_model submissions highlighted in a distinct color. The visual companion to catalog.leaderboard. On the pretrained_few_trial track the figure also includes the classical LOSO baseline as…
stream.telemetryLive snapshot of a streaming session: latest prediction + recent window, signal quality (meanChannelQuality, snrDb, artifactProbability), indicators, running stats. Poll this while a session runs. Works with OR without a deployed model: modelless hardware streams (no classifier)…
data.uploadUpload 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 — uploads without it are rejected (no silent…
data.delete_uploadDelete an uploaded file from your account (disk + registry). Requires confirm=True. Pass the path returned by data.upload (file.path). Only your own uploads can be deleted; missing paths return not-found.
data.inspect_datasetExploratory 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…
data.inspect_fileExploratory 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…
project.createCreate a project (container for one pipeline document). Returns projectId.
project.listList projects owned by the current principal (agent work included).
project.saveSave a pipeline graph into a project (visible on the studio canvas). Always pass expected_revision from the last project.load (or a prior save/create response). When it does not match the current server revision, the save fails with a conflict — it does NOT silently overwrite co…
project.loadLoad a project's saved pipeline (train graph + meta) for editing/re-running.
project.renameRename a project you own (max 200 characters).
project.deleteDelete a project (and its saved pipeline document) from the studio workspace. Requires confirm=True: this removes the project from the user's project list. Executions already run from it are not affected. Find ids with project.list.
protocol.validateValidate a neurofeedback protocol document WITHOUT saving it. Returns {valid: true, spec} on success, or {valid: false, errors: [...]} with the per-violation list (loc/msg) when the document is invalid — the verdict is data, never an exception.
protocol.saveValidate and persist the NEXT VERSION of a protocol document (confirm-gated: each call appends a version row; accidental calls clutter the version history). Edits create a new version — the assignment pins the version it runs.
protocol.listEvery saved protocol id at its latest version.
protocol.start_assignmentEnroll a subject and prepare their neurofeedback deploy session (confirm-gated — a session runs on a human). Creates the assignment (yoked sham arms REQUIRE yoked_donor_upload_id: a recorded simulated session's upload — mint one by running an active-arm session and data.upload-i…
protocol.session_statusLive snapshot of a protocol deploy session: the scheduler block / session progress + threshold state (under ``protocol``) beside the telemetry window (recent indicators, quality, stats).
protocol.pausePause a running protocol session (blocks freeze; the session and device stay alive — resume with protocol.resume). Simulated and playback sessions only while paused: no data is recorded.
protocol.resumeResume a paused protocol session (restarts the server-driven device loop). Live-hardware resume requires a Pro plan; simulated sessions are free. Pass the session's channel count (start_assignment reports it).
protocol.stopStop a protocol session (always safe to call): stops streaming, saves the block-annotated recording, advances the assignment when the scheduler reached its natural end, and disconnects the device (best-effort — a failed disconnect is reported, never raised).
protocol.export_statsPaired pre/post analysis for one assignment's recorded sessions: per-block aggregates (descriptive + CSV), paired t / Wilcoxon / dz (per-statistic minimums: paired t and Cohen's dz need ≥ 3 completed sessions, Wilcoxon needs ≥ 5), specificity notes, the adaptive-threshold trajec…
python.validateStatically check a python_model script (BYO model class) without running it. Compiles the source and verifies the contract — a class with fit(X, y, info) and predict(X); optional predict_proba(X) unlocks confidence panels. Never executes the code, so it is safe against the hoste…
stats.groupedCluster-aware group statistics across executions. REQUIREMENT (read first): each execution must have been run with an evaluation plan that STORED per-SUBJECT accuracies for at least 3 subjects — e.g. leave-one-subject-out (LOSO) or grouped CV over >= 3 subjects. Session groups w…
foundation.compareRun 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 trial…
Details
| Endpoint | https://nimbus-mcp.fly.dev/mcp (Streamable HTTP) |
| Package | [email protected] (PYPI, stdio) |
| First published | 3 Nov 2026 |
| Last updated | 3 Nov 2026 |
| Registry name | io.github.nimbusbci/nimbus-mcp |
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