Nimbus BCI
by FastDrop
AI agents build, train, and analyze BCI/EEG pipelines: data, models, experiments, live sessions.
AI agents build, train, and analyze BCI/EEG pipelines: data, models, experiments, live sessions. Official MCP Registry: io.github.nimbusbci/nimbus-mcp@0.5.1. Package/stdio server - no remote endpoint to probe.
Problem it solves
Discoverable MCP server listed in the official MCP Registry.
How it's different
Package server from the official registry (no remote probe).
Ingested from the official MCP Registry
FastDrop pulled this entry from the official MCP Registry as io.github.nimbusbci/nimbus-mcp and probed the endpoint. The maker did not submit it, so the outbound link is not endorsed
and the description is not theirs.
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Read directly from the endpoint on 9 Oct 2026. Not supplied by the maker.
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 guida
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.
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. Rankings are per-dataset under the canonical ``within_session`` protocol (see ``protocol``). Within each dataset, ``rows`` are sorted desc
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().
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