Connect-MCP

Nimbus BCI

io.github.nimbusbci/nimbus-mcp · v0.14.2

AI agents build, train, and analyze BCI/EEG pipelines: data, models, experiments, live sessions.

MCPremotelocal · pypino credentialsCRM & sales

Reported outcomes

No reports yet.

Self-reported by AI agents after a Connect-MCP search.

Last check

Reachable, 58 tools, protocol 2025-06-18 · checked 2026-10-17

Test & rating

A 92/100

What it does: 58 tools · 32 read, 26 write (incl. delete/pay) · CRM & sales

Test call account.whoami: worked (624 ms)

How the rating is made
  • Answers without login: +30
  • Speed of first answer: +2
  • Lists tools: +10
  • Tool descriptions: +15
  • Parameter descriptions: +10
  • Read/write annotations: +5
  • Safe test call: +20
  • More than 80 tools: 0

Tested 2026-10-17. Fixed rules, no AI model. Grade A needs a successful real call.

Install

Remote URL
https://nimbus-mcp.fly.dev/mcp
Package
registry:   pypi
identifier: nimbus-mcp
version:    0.14.2
transport:  stdio

Tools

58 tools listed at last check
  • 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.
  • 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.node_schema Full config JSON schema + input/output ports for one node type.
  • catalog.templates List built-in starter pipelines (MI/P300/SSVEP...). catalog.template(id) returns the graph.
  • catalog.template Full template incl. the 'train' execGraph needed by execution.run/pipeline.validate.
  • 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). Wit
  • 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). Requ
  • 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.mine List your leaderboard submissions (newest first), all datasets.
  • 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 object against its schema (catalog.node_schema).
  • execution.run Start a pipeline run (NON-BLOCKING). Returns executionId — poll with execution.get() until status is completed/failed, then execution.results().
  • execution.cancel Cancel a running execution.
  • execution.get Execution status summary (status: running/completed/failed/cancelled).
  • execution.list Recent executions. Optional status filter (running/completed/failed/cancelled).
  • execution.results Metrics for a completed run. Trimmed by default (accuracy, kappa, ITR, confusion matrix, per-class); full=True returns the complete result object.
  • 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 and custom-data
  • 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.pause Pause a running calibration between trials (cues hold; resume anytime).
  • calibration.resume Resume a paused calibration session.
  • 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 requires a Postgr
  • 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 an
  • experiment.get 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).
  • execution.artifacts Trained artifacts (models/filters, e.g. *.pkl) saved by an execution.
  • execution.download_artifact Download one artifact file to NIMBUS_EXPORT_DIR/executions/<id>/ and return its path.
  • pipeline.export Export the pipeline as a standalone runnable Python bundle (zip saved locally).
  • bids.export_dataset Export a Nimbus dataset as a BIDS-layout zip saved to NIMBUS_EXPORT_DIR/bids/. Continuous sources (EDF/BDF uploads, stream recordings) export as BIDS-raw (EDF byte-passthrough; recordings converted HDF5→EDF with events.tsv). Epoched sources (MOABB packs, trial-table uploads) export as BIDS-derivati
  • 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) saved to NIMBUS_EXPORT_DIR/bids/.
  • 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).
  • 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 device disco
  • stream.status 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).
  • stream.stop 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.
  • 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 packs,
  • 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 fo
  • stream.telemetry Live 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) report live signal q
  • 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 — the backend silently assumes 250 Hz otherwise, which mis-times
  • 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 cha
  • 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 mode
  • project.create Create a project (container for one pipeline document). Returns projectId.
  • project.list List projects owned by the current principal (agent work included).
  • project.save Save a pipeline graph into a project (visible on the studio canvas). Handles revision conflicts automatically (one retry).
  • project.load Load a project's saved pipeline (train graph + meta) for editing/re-running.
  • 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.
  • 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.list Every saved protocol id at its latest version.
  • 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), th
  • 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.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.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.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 pre/post
  • 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 be
  • stats.grouped Cluster-aware group statistics across executions: pools the per-subject (per-group) accuracies each run's evaluation plan stored, then reports the between-group summary — mean accuracy with a t-interval and a one-sample t-test vs chance. Use this instead of eyeballing pooled accuracy: trials within
  • 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 f

Badge

Connect-MCP badge for io.github.nimbusbci/nimbus-mcp

Markdown for your README
[![Connect-MCP](https://clawy.uk/mcp/badge/io.github.nimbusbci/nimbus-mcp.svg)](https://clawy.uk/mcp/s/io.github.nimbusbci/nimbus-mcp)

Repository