Nimbus BCI

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

• 连接方式: 电脑或云端
• 传输方式: stdio, Streamable HTTP
• 分类: 数据库与数据集
• 你需要填写：NIMBUS_TOKEN, NIMBUS_MCP_KEY

安装

选择你使用的 AI 应用，然后复制设置。

通过 5/5 项检查 · 你的提问会经过开发者的服务器（nimbus-mcp.fly.dev）

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

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

{
  "servers": {
    "nimbus": {
      "type": "http",
      "url": "https://nimbus-mcp.fly.dev/mcp"
    }
  }
}

claude mcp add nimbus --env NIMBUS_API_URL=your-value --env NIMBUS_TOKEN=your-value --env NIMBUS_TOKEN_FILE=your-value --env NIMBUS_MCP_KEY=your-value --env NIMBUS_MCP_KEY_FILE=your-value -- uvx nimbus-mcp

{
  "mcpServers": {
    "nimbus": {
      "command": "uvx",
      "args": [
        "nimbus-mcp"
      ],
      "env": {
        "NIMBUS_API_URL": "your-value",
        "NIMBUS_TOKEN": "your-value",
        "NIMBUS_TOKEN_FILE": "your-value",
        "NIMBUS_MCP_KEY": "your-value",
        "NIMBUS_MCP_KEY_FILE": "your-value"
      }
    }
  }
}

{
  "mcpServers": {
    "nimbus": {
      "command": "uvx",
      "args": [
        "nimbus-mcp"
      ],
      "env": {
        "NIMBUS_API_URL": "your-value",
        "NIMBUS_TOKEN": "your-value",
        "NIMBUS_TOKEN_FILE": "your-value",
        "NIMBUS_MCP_KEY": "your-value",
        "NIMBUS_MCP_KEY_FILE": "your-value"
      }
    }
  }
}

{
  "servers": {
    "nimbus": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "nimbus-mcp"
      ],
      "env": {
        "NIMBUS_API_URL": "your-value",
        "NIMBUS_TOKEN": "your-value",
        "NIMBUS_TOKEN_FILE": "your-value",
        "NIMBUS_MCP_KEY": "your-value",
        "NIMBUS_MCP_KEY_FILE": "your-value"
      }
    }
  }
}

哪些应用可以使用它

依据这个服务器的连接方式和各应用自己的文档，检查于 2026年11月10日。

通过互联网连接，适用于

ClaudeChatGPTGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatOpen WebUIRaycast

在你的电脑上运行，适用于

ClaudeGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatRaycast

全部应用及其优缺点 →

它能做什么

工具名称和描述与服务器在 2026年11月11日 报告的完全一致。这些内容由作者撰写，不是我们写的；我们以纯文本形式显示。

• 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

• 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" — the id catalog.leaderboard and catalog.datasets use) and ``paradigm`` when the template loads a public dataset.

• 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 `

• 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 m

• 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 (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).

• 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 (ca

• 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.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). ``ti

• 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.datas

• 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 n

• 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 hando

• 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() f

• 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`` / ``kin

• 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/ / 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.

• 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) a

• 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 event

• 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

• 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

• 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 inspec

• 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

• 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)

• 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 2

• 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)

• 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 a

• 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). 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 con

• 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.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.

• 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-in

• 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 traject

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月12日

• ✓ 通过

  作者与仓库一致

  注册名称已通过 GitHub 账户 nimbusbci 验证，该账户拥有此仓库

• ✓ 通过

  仍在维护

  代码最后更新于7天前

• ✓ 通过

  有许可证

  使用 Apache-2.0

• ✓ 通过

  端点有响应

  在 4709 毫秒内响应（HTTP 200）

• – 不适用

  使用 OAuth 登录

  无需登录

• ✓ 通过

  工具描述中没有隐藏指令

  已读取 60 个工具描述，未发现针对 AI 的隐藏指令

用户评价

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与“数据库与数据集”中的其他服务器比较i

• 星标数多于此分类中其他 1170 个服务器的 86%
• 发布到注册中心的时间比此分类中其他 1170 个服务器的 83% 更新
• 基础检查结果等于或优于此分类中其他 1170 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：↓ 每周下载量i★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Dbx

Query databases from AI agents using connections configured in DBX.

作者：t8y2·广泛使用·↓ 9446/周·★ 3万·6个月前更新

在你的电脑上运行

安装

MCP Toolbox for Databases

MCP Toolbox for Databases enables your agent to connect to your database.

作者：googleapis·广泛使用·★ 1.7万·6天前更新

在你的电脑上运行

安装

MCP Toolbox for Databases

MCP Toolbox for Databases enables your agent to connect to your database.

作者：googleapis·广泛使用·★ 1.7万·8个月前更新

在你的电脑上运行

安装

作者的 README

{

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

{

{

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

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

{

   nimbus-mcp login     # options: --api-url URL, --ttl-days 7..90, --name NAME

   {

account.whoami() → {userId, email, plan: {isPro, pioneerAccess},

- name: Nimbus smoke run