Nimbus BCI README

nimbus-mcp

MCP server that lets AI agents (Claude Code, Cursor, Claude Desktop) build, validate, run, and analyze Nimbus BCI pipelines — upload their own EEG data, persist pipelines into studio projects, run multi-configuration experiment campaigns, watch live EEG sessions, and (explicitly confirmed) start live streaming — through your local Nimbus backend or the hosted deployment, with one nimbus-mcp login.

Why nimbus-mcp (October 2026 field)

The BCI-MCP niche grew four other servers in 2026. They are honest, focused tools — and each covers a slice of what nimbus-mcp does end-to-end:

Scope honesty (deliberate non-goals): no stimulation output (eeg-mcp's triple-interlocked send_stim_event is genuinely novel; we don't drive stimulators), no spike/ephys data (ephys-mcp covers intracortical recording — different data plane), no source localization / full MNE parity (mne-mcp is a deep offline MNE wrapper; plots.* covers the figures agents need on Nimbus data).

Install

pip install nimbus-mcp   # or: uvx nimbus-mcp

(Also installable from the repo: pip install -e nimbus-studio/mcp.)

Zero-hardware demo: examples/demo.md — a copy-paste agent prompt that runs the whole loop (explore → inspect → figures → train → confusion figure → leaderboard → stats) on public MOABB data, no device.

Authentication

Three ways to give the server a credential — tried in this order at startup:

1. nimbus-mcp login (recommended — hosted API, no token pasting). A device-code login: the command prints a URL and an 8-character code, opens your browser, you approve in Nimbus Studio, and the minted token is stored at ~/.nimbus/credentials.json (0600) and picked up automatically on every future start.

   bashCopy

   nimbus-mcp login     # options: --api-url URL, --ttl-days 7..90, --name NAME
   nimbus-mcp status    # doctor: credential source, plan, quota, days-to-expiry, live probe
   nimbus-mcp logout    # remove the stored credential
   

   After a login, MCP client configs need no secret at all:

   config.jsonCopy

   {
     "mcpServers": {
       "nimbus": {
         "command": "uvx",
         "args": ["nimbus-mcp"],
         "env": { "NIMBUS_API_URL": "https://nimbus-studio.fly.dev" }
       }
     }
   }
   

2. Desktop app — zero config. Just have the Nimbus Studio desktop app running: its local key file is auto-discovered (macOS ~/Library/Application Support/Nimbus Studio/mcp-key.json, Linux ~/.config/Nimbus Studio/…, Windows %APPDATA%\Nimbus Studio\…) and the server talks to the local backend at http://127.0.0.1:8080. Nothing to paste or configure.

3. Environment variables (advanced / CI). NIMBUS_TOKEN (a hosted API token nimb_… minted in Nimbus Studio → Account → API tokens) or NIMBUS_TOKEN_FILE (a 0600 JSON file {"token": "…"} — keeps the secret out of process env and MCP configs), or the local pair NIMBUS_MCP_KEY / NIMBUS_MCP_KEY_FILE (must match MCP_LOCAL_KEY on a local backend — see below). Explicit env always beats files on disk.

No credential anywhere? The server still starts — in setup mode. Every tool call returns {ok: false, setupRequired: true, message, options} with the three paths above, so your agent walks you through onboarding instead of the server crashing. A hosted token rejected mid-session (expired or revoked) returns the same shape, including how many days ago it expired and a nimbus-mcp login first option.

Checking who you are

account.whoami() → {userId, email, plan: {isPro, pioneerAccess},
            freeRuns: {monthlyLimit, remaining},
            token: {name, expiresAt, daysLeft} | {masked} | null,  # {masked} = gateway header token
            source}                                      # store | env | token_file | header | …

Call account.whoami() from the agent to see the account, plan, this month's free-run quota, and (in token mode) the token's days-to-expiry; nimbus-mcp status is the terminal equivalent with a live backend probe.

What a hosted token means

• The token IS you. Requests run under your account: executions appear in your studio history and your plan's quotas and limits apply — there is no separate agent allowance. When the free monthly quota is exhausted, run errors carry the upgrade link https://studio.nimbusbci.com/pricing?reason=mcp-quota.
• CPU-only in v0.4. Token-authenticated runs do not hydrate cloud GPUs.
• Rotation. Tokens live at most 90 days (30 by default). Plan changes are snapshotted at mint time — after an upgrade, re-login (or revoke and re-create the token) to pick up the new plan. An expired token surfaces as setup guidance with the day count, not a dead end.

Requirements (local mode)

• A Nimbus backend running locally: the desktop app, or the dev server (cd nimbus-studio/backend-py && python -m nimbus_backend.server.app) with DEBUG=1.
• The backend started with MCP_LOCAL_KEY=<some-secret> (never set this on Fly — it is refused there).
• Desktop app users: open Settings → MCP & Agents — no manual key setup (the app creates the key, injects it into its backend, and hands you copy-ready configs).

No desktop app required. Acquisition is fully in-backend: a headless local backend + nimbus-mcp can drive real hardware (BrainFlow boards, BrainBit over BLE, LSL, PiEEG) and synthetic boards, with zero Electron:

git clone <this repo> && cd NimbusStudio/nimbus-studio/backend-py
pip install -e ".[streaming]"            # brainflow + pyneurosdk2 + pylsl extras
export DESKTOP_LOCAL_BACKEND=true MCP_LOCAL_KEY=<secret> MCP_LOCAL_USER_ID=user_…
python -m nimbus_backend.server.app      # loopback backend with device radios
# another shell: nimbus-mcp against http://127.0.0.1:8080 with NIMBUS_MCP_KEY=<secret>

The same stream.start → stream.telemetry flow then works with OR without a trained model (modelless streams report live signal quality + focus/relaxation indicators).

Configure the backend

Desktop/dev env (e.g. backend-py/data/.env or the dev shell):

MCP_LOCAL_KEY=choose-a-long-random-string
MCP_LOCAL_USER_ID=user_your_clerk_user_id
DEBUG=1   # dev server only; the desktop app qualifies automatically

MCP_LOCAL_USER_ID sets the principal the MCP key authenticates as. Set it to your own Clerk user id (user_…) so everything the agent creates — projects, saved pipelines, executions — appears in your studio UI as yours. Pick one owner and stick with it: switching the id mid-life splits ownership of agent-created work across two principals, and neither identity then sees the whole history.

Watchdog default: streaming sessions started through MCP are auto-stopped after 15 minutes with no one watching (every stream.status / stream.telemetry poll resets the timer). Pass idle_timeout_sec=0 to stream.start to disable it for a session.

When enabling MCP_LOCAL_KEY on a machine connected to an untrusted network, also set HOST=127.0.0.1 on the backend. The 0.0.0.0 default (settings.host) applies to the bare dev server (python -m nimbus_backend.server.app), so with it the key would otherwise be accepted from the LAN; backend-py/scripts/run_server.py already defaults to 127.0.0.1, and the desktop app pins loopback itself.

Run the server

cd nimbus-studio/mcp
python -m venv .venv && source .venv/bin/activate
pip install -e ".[test]"
NIMBUS_MCP_KEY=choose-a-long-random-string python -m nimbus_mcp

Env vars: NIMBUS_API_URL (default http://127.0.0.1:8080, or the store's api_url after a login), NIMBUS_TOKEN / NIMBUS_TOKEN_FILE (hosted API token — see Authentication), NIMBUS_MCP_KEY (must match MCP_LOCAL_KEY), NIMBUS_MCP_KEY_FILE (path to a 0600 JSON file {"key": "…"} — the desktop app's one-click MCP setup writes it; consulted only when NIMBUS_MCP_KEY is unset), NIMBUS_EXPORT_DIR (default ~/nimbus-exports). With none of the token/key vars set, the login store and then the desktop key file are auto-discovered; with nothing found, the server runs in setup mode (every tool returns onboarding guidance).

Hosted gateway (streamable HTTP)

nimbus-mcp serve runs the same 48 tools over streamable HTTP instead of stdio — for remote MCP clients, registries (Smithery lists URL-based servers), and browser-side clients:

nimbus-mcp serve --port 8080   # env: NIMBUS_MCP_HOST/PORT/PATH

The gateway binds 127.0.0.1 by default — exposing it (--host 0.0.0.0) is explicit opt-in; combine that with the per-request tokens below (the gateway itself holds no credential to gate access with).

The gateway holds no credential itself: each request's Nimbus API token arrives as a header, so one shared deployment serves many users as themselves. Calls without a header get setup guidance (add the header, or install locally via uvx).

{
  "mcpServers": {
    "nimbus": {
      "type": "http",
      "url": "https://nimbus-mcp.fly.dev/mcp",
      "headers": { "X-Nimbus-Token": "nimb_…  (Account → API tokens)" }
    }
  }
}

(A gateway started with NIMBUS_TOKEN in the environment uses it as the fallback for headerless calls — single-tenant self-hosting.)

Claude Code

# --env flags go BEFORE the -- separator (everything after it is the literal
# server command, so the after-form would feed --env to python/uvx):
claude mcp add nimbus --env NIMBUS_MCP_KEY=choose-a-long-random-string \
  -- <path-to-mcp-venv>/bin/python -m nimbus_mcp

Cursor / Claude Desktop (stdio)

{
  "mcpServers": {
    "nimbus": {
      "command": "<path-to-mcp-venv>/bin/python",
      "args": ["-m", "nimbus_mcp"],
      "env": { "NIMBUS_MCP_KEY": "choose-a-long-random-string" }
    }
  }
}

Tools (48)

Auth: account.whoami (account, plan, quota, token expiry) Discovery: catalog.nodes, catalog.node_schema, catalog.templates, catalog.template, catalog.datasets, catalog.leaderboard Data: data.upload Inspect: data.inspect_dataset, data.inspect_file (EDA: channels, class balance, band powers, PSD) Build: pipeline.validate, pipeline.validate_node Run: execution.run (non-blocking), execution.get, execution.list, execution.results, execution.cancel Campaigns: experiment.run (non-blocking, 1-25 paced runs), experiment.get Artifacts: execution.artifacts, execution.download_artifact, pipeline.export Live: device.list, device.test, stream.start (needs confirm=true), stream.status, stream.telemetry, stream.stop Calibration: calibration.start (needs confirm=true), calibration.status, calibration.pause, calibration.resume, calibration.train Projects: project.create, project.list, project.save, project.load Python: python.validate (static contract check for BYO model code — no execution) Leaderboard: leaderboard.submit (BYO python_model → server-side LOSO scoring, 202 + submissionId; track="pretrained_few_trial" = efficiency track: 100-trial-capped LOSO, torch code must declare PARAMS_TRAINED), leaderboard.status (poll one submission, owner-only), leaderboard.mine (your submissions, newest first) BIDS: bids.export_dataset (pack / upload / recording → BIDS-layout zip; continuous → BIDS-raw, epoched → BIDS-derivatives), bids.export_execution (results bundle: metrics, participants.tsv, protocol, pipeline snapshot) Plots: plots.confusion, plots.dataset (psd / topomap / bandpower / spectrogram), plots.erp (per-class butterfly), plots.leaderboard (CI whisker bars, BYO highlighted; track=pretrained_few_trial overlays the classical LOSO baseline side-by-side) — PNG figures the assistant can SEE Stats: (cluster-aware per-subject accuracies across runs: t-interval + t-test vs chance)

Not sure which pipeline to build? catalog.leaderboard() ranks benchmarked pipelines per dataset (meanAccuracyPct desc, 95% CI) under the canonical within_session protocol — agents pick templates by ranking there and pull the winner with catalog.template(pipelineId).

Look at your data first (and at your results)

Before building any pipeline, agents can see the data: channels, sampling rate, trial/class balance, per-channel µV stats, canonical band powers, and a PSD overview — for a public dataset or a file on disk.

"Inspect BNCI2014_001 subject S01 before we pick a pipeline."

data.inspect_dataset(dataset="BNCI2014_001", subject="S01", mode="all")
# → {channels: {count: 22, names: [...], flatlined: []}, samplingRate: 250,
#    trials: {count: 288, classLabels: [...], classCounts: {...}},
#    channelStats: [...], bandPowers: {...}, psd: {...},
#    computedFrom: {nSamples: ..., sampleStrategy: "stratified_sample_seed42_4_of_20"}}

subject is required (e.g. "S01"; get the list via catalog.datasets) and a comma-list like "S01,S03" loads a cohort; mode is training | evaluation | all.

The same works for files: data.inspect_file picks the source from the path shape. An ABSOLUTE path reads the file from disk (local backend only — desktop app / MCP local mode: no upload step, the data never leaves the machine); a RELATIVE path — the one data.upload returns — describes the uploaded file on ANY backend (hosted or local):

"Look at ~/recordings/session-01.edf and tell me if the montage is sane."

data.inspect_file(path="/Users/you/recordings/session-01.edf")  # absolute → local file
data.inspect_file(path="uploads/<user>/session-01.edf")         # data.upload path → upload

On a hosted backend absolute paths are refused — data.inspect_file then returns guidance (data.upload the file and pass the returned path back, or point the server at a local backend) instead of a dead end.

Why inspect first: class balance drives stratification (imbalanced classes skew accuracy), and flatlined channels mean a montage/reference problem worth fixing before training. And a units caveat: the loader assumes volts — a µV-native CSV reads 1e6x too large; set unitsScale (e.g. 1e-6 with units: "uV") in the pipeline's custom_data config when needed.

The same data — and your results — can be rendered as PNG figures the assistant can look at, not just JSON: plots.dataset (PSD / band-power heatmap / scalp topomap / spectrogram), plots.erp (per-class evoked butterfly — check class structure before training anything), plots.confusion (after a run: which classes are actually confused), plots.leaderboard (CI whisker bars per dataset, BYO submissions highlighted). save=true also writes the PNG into NIMBUS_EXPORT_DIR/plots/.

plots.confusion(execution_id="exec_123")               # → image + per-class accuracy
plots.dataset(kind="psd", source="dataset", dataset="BNCI2014_001", subject="S01")
stats.grouped(execution_ids=["exec_123", "exec_124"])  # cluster-aware t-interval + p

Uploading data

Bring your own recordings instead of (or alongside) the public datasets.

"I have a .edf recording at ~/recordings/session-01.edf — upload it and build a pipeline around it."

The agent calls data.upload(file_path=…), which registers the file with the backend and returns the stored path; that path goes into a custom_data node's config ({"filePath": "<path>", "format": "edf", …}) for pipeline.validate / execution.run / experiment.run. For plain CSV/TSV/TXT without embedded metadata, pass sampling_rate (Hz) — the backend silently assumes 250 Hz otherwise; format overrides extension-based detection.

Experiment campaigns

One experiment.run call = a paced sweep of 1-25 pipelines (at most 2 training runs in flight) with aggregated metrics, instead of the agent babysitting 25 individual execution.run polls.

"Compare CSP-LDA vs EEGNet on BNCI2014_001 across subjects 1-3."

The agent builds six train graphs, calls experiment.run(runs=[{name: "csp-lda-s1", train_graph: …}, …]), gets an experimentId back immediately, then polls experiment.get(experiment_id) until status is completed — per-run status and, at the end, aggregates like {"kappa": {"mean": 0.61, "std": 0.08, "best": {name, value}}} (mean/std/best over completed runs only).

Working with projects

Agent builds, human inspects. Pipelines the agent saves land in real studio projects, so you can open the canvas and see exactly what ran.

"Save this pipeline as a project called 'motor-imagery-baseline' — I'll review it in the studio."

project.create(name) makes the container, project.save(project_id, train_graph) writes the graph (layout auto-generated, revision conflicts retried once) and project.load(project_id) reads it back for editing or re-running. With MCP_LOCAL_USER_ID set to your user id, the project shows up in your studio project list.

Watching a live session

While a streaming session runs, the agent can watch its telemetry and tell you what it sees.

"Watch my focus session and tell me when signal quality drops."

The agent polls stream.telemetry(session_id) — latest prediction, the recent window, signal quality (meanChannelQuality, snrDb, artifactProbability) and running stats — and warns when quality degrades. Each poll also resets the idle watchdog, so a session under active watch is never auto-stopped; an abandoned one is shut down after 15 minutes.

Calibrating a subject

Guided calibration turns a person wearing the device into their own training data: a cue-guided session records labelled trials, and the agent then trains the subject's own classifier from the recording.

"Run a 10-trials-per-class MI calibration on my BrainBit, then train my personal classifier when it's done."

The five-tool flow (all non-blocking):

# 1. Start (confirm-gated): fetches the paradigm's calibration template
#    (mi / p300 / sart / target_hit), patches in device + trial count, and
#    starts the session. The Studio app shows the cues on its calibration
#    dashboard automatically.
calibration.start(paradigm="mi", trials_per_class=10, confirm=true)
# → {started: true, executionId: "…"}

# 2. Poll: phase (baseline → imagery trials → complete), current trial, cue,
#    progress tally, and an ETA estimate from the timing SSOT.
calibration.status(execution_id="…")
# → {phase: "imagery", currentTrial: {index: 7, total: 20, class: "Left Hand",
#    cue: "← LEFT"}, progress: {trialsDone: 6, …}, estimatedRemainingSec: 105.0, …}

# 3./4. Pacing between trials (cues hold; resume anytime).
calibration.pause(execution_id="…")   # calibration.resume(...) to continue

# 5. When phase == "complete" the snapshot carries the recording:
#    calibration: {uploadId, path, filename, format}. Train from it.
calibration.train(execution_id="…")
# → {trainExecutionId: "…", calibrationUploadId: "…"} → poll execution.get,
#    then execution.results for the metrics (kappa, accuracy, …).

mi trains on the default mi_headband_csp_lda template (CAR → 8-30 Hz bandpass → CSP → LDA); p300 / sart / target_hit have no default train template — pass an explicit template_id (from catalog.templates) or your own train_graph to calibration.train. Either way the first data node is rewired onto the recorded upload (custom_data source pinned to the uploadId), preserving the template's training config.

calibration.start refuses to run without confirm=true — like stream.start, the device goes on a human's head; call device.test first.

Pro plan required — calibration workflows and custom-data training are freemium-gated (free-tier requests are 403 by the node policy). Local X-MCP-Key principals pass when the desktop app flags the signed-in Pro session (MCP_LOCAL_IS_PRO); otherwise the node policy applies to them too.

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.

Safety

stream.start refuses to run without confirm=true — it connects an EEG device and starts a live session on a human. The X-MCP-Key path is machine-local only (never accepted on Fly deployments); hosted mode authenticates with a personal Authorization: Bearer token from nimbus-mcp login (see Authentication above). Sessions started via MCP are stopped automatically after 15 idle minutes (see the watchdog note above).