bots.st/nimbus-bci
Nimbus BCI
Export datasets and execution results to BIDS
6091 ms
answered our call
- State
- Answered
- Protocol
- MCP 2025-06-18
- Tools listed
- 63
- Answered
- 3 of the last 3 calls
How to reach it
- MCP, HTTP
https://nimbus-mcp.fly.dev/mcp - PyPI
uvx nimbus-mcpReleased 3 Nov 2026, version 0.14.21
What it can do
| Tool | What it says it does |
|---|---|
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. In anonymous Local Free mode there is no… |
bids.export_dataset | Export 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 events.tsv). Epoched… |
bids.export_execution | Export 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 ``contentBase64`` (up to 1 MB of… |
calibration.pause | Pause a running calibration between trials (cues hold; resume anytime). |
calibration.resume | Resume a paused calibration session. |
calibration.start | 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… |
calibration.status | 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. |
calibration.train | 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 resolves on… |
catalog.datasets | Curated public EEG datasets (MOABB packs) available to pipelines. |
catalog.leaderboard | 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)… |
catalog.node_schema | Full config JSON schema + input/output ports for one node type. |
catalog.nodes | List Nimbus pipeline node types (data, preprocessing, features, models...). Use catalog.node_schema(node_type) for one node's full config schema and ports. |
catalog.template | Full template incl. the 'train' execGraph needed by execution.run/pipeline.validate. |
catalog.templates | List 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. |
data.delete_upload | Delete 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_dataset | 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… |
data.inspect_file | 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… |
data.upload | 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 — uploads without it are rejected (no silent 250 Hz… |
device.list | EEG devices supported by this backend (OpenBCI, Muse, BrainBit, LSL, PiEEG...). |
device.test | Test a device connection WITHOUT starting a stream (safe, no confirm needed). |
execution.artifacts | Trained artifacts (models/filters, e.g. *.pkl) saved by an execution. |
execution.cancel | Cancel a running execution. |
execution.delete | Permanently 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.download_artifact | Download 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. |
execution.get | Execution 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). ``title`` is the run… |
execution.list | Recent 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.dataset). ``title`` is… |
execution.results | Metrics for a completed run. Trimmed by default (accuracy, kappa, ITR, confusion matrix, per-class); full=True returns the complete result object. |
execution.run | Start 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 (cancel one… |
experiment.delete | Delete 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.get | Experiment 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`` / ``kindBreakdown``… |
experiment.run | 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… |
foundation.compare | 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… |
leaderboard.mine | List your leaderboard submissions (newest first), all datasets. |
leaderboard.status | Fetch 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.submit | Submit 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 minutes (deep)… |
pipeline.export | Export 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) are left out… |
pipeline.validate | 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(). |
pipeline.validate_node | Validate 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). |
plots.confusion | Confusion-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.dataset | Figure 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 inspect uses: MOABB… |
plots.erp | ERP 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.leaderboard | Leaderboard 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 a muted side panel… |
project.create | Create a project (container for one pipeline document). Returns projectId. |
project.delete | Delete 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. |
project.list | List projects owned by the current principal (agent work included). |
project.load | Load a project's saved pipeline (train graph + meta) for editing/re-running. |
project.rename | Rename a project you own (max 200 characters). |
project.save | Save 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 concurrent edits… |
protocol.export_stats | Paired 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 trajectory, and a… |
protocol.list | Every saved protocol id at its latest version. |
protocol.pause | Pause 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.resume | Resume 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.save | Validate 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.session_status | Live snapshot of a protocol deploy session: the scheduler block / session progress + threshold state (under ``protocol``) beside the telemetry window (recent indicators, quality, stats). |
protocol.start_assignment | Enroll 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-ing its recording)… |
protocol.stop | Stop 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.validate | Validate 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. |
python.validate | Statically 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 hosted backend. Use it… |
stats.grouped | Cluster-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 within one subject… |
stream.start | 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… |
And 3 more tools.
In its maker's words
BCI/EEG toolkit: public MOABB/BNCI datasets, pipeline build/validate, hosted training, BIDS export.
- Source
- github.com/nimbusbci/nimbus-mcp
- Publisher
- nimbusbci
- Listed from
- the official MCP Registry as
io.github.nimbusbci/nimbus-mcp - Version
- 0.14.21, updated 3 Nov 2026
- Kinds of work
- Data and databases, Documents and files, AI models and agent tools
Every call we made
| When | What happened | Time |
|---|---|---|
| Answered | 6091 ms | |
| Answered | 2386 ms | |
| Answered | 790 ms |
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