io.github.nimbusbci/nimbus-mcp

up checked 2026-11-11T07:40:13.835Z

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Publisher-provided title

Nimbus BCI

Publisher-provided description

BCI/EEG toolkit: public MOABB/BNCI datasets, pipeline build/validate, hosted training, BIDS export.

Summary by lookupd

Lets agents run EEG/BCI pipelines on public datasets, calibrate and train classifiers, and export BIDS data and results.

Install

Claude Code, remote (streamable-http)

claude mcp add --transport http nimbus https://nimbus-mcp.fly.dev/mcp

Claude Code, pypi package (stdio)

claude mcp add nimbus -- uvx nimbus-mcp

Codex, remote (streamable-http)

codex mcp add nimbus --url https://nimbus-mcp.fly.dev/mcp

Codex, pypi package (stdio)

codex mcp add nimbus -- uvx nimbus-mcp

Cursor, remote (streamable-http) → .cursor/mcp.json

{
  "mcpServers": {
    "nimbus": {
      "url": "https://nimbus-mcp.fly.dev/mcp"
    }
  }
}

Cursor, pypi package (stdio) → .cursor/mcp.json

{
  "mcpServers": {
    "nimbus": {
      "command": "uvx",
      "args": [
        "nimbus-mcp"
      ]
    }
  }
}

Other clients: docs. Or tell your agent: Set up lookupd for me: read https://lookupd.com/agent.md and follow it.

Tools (showing 30 of 63)

Publisher-provided text

• 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 e
• 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. Each row carries ``datasetId`` (catalog id, e.g. "BNCI2014_001" — th
• 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``
• 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, neve
• leaderboard.status Fetch one of your leaderboard submissions (owner-only). Returns status (queued | scoring | done | failed), metrics when done (meanAccuracyPct + CI, same shape
• 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
• 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
• 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
• 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_no
• execution.get Execution status summary (status: running/completed/failed/cancelled). Summary fields: ``dataset`` is the human-readable display label (e.g. "BCIIV2a"); ``data
• execution.list Recent executions. Optional status filter (running/completed/failed/cancelled). Per-run fields: ``dataset`` is the human-readable display label (e.g. "BCIIV2a"
• 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 calibr
• 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
• 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_
• 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_co
• 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 e
• experiment.get Experiment snapshot: status (running/completed/failed), per-run rows ({name, executionId, status, error?, metrics?, evaluationKind?}) and, once finished, aggreg
• execution.artifacts Trained artifacts (models/filters, e.g. *.pkl) saved by an execution.
• 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`
• 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 g
• 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 source

Details

• Version: 0.14.21
• Transports: streamable-http, stdio
• Uptime (7 days): 100%
• Median latency: 248 ms
• First indexed: 2026-11-01T09:43:06.945Z
• Last seen in registry: 2026-11-03T22:17:22.509Z
• Repository: https://github.com/nimbusbci/nimbus-mcp

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