_ registry / mcp http-sse · checked 12h ago

nimbus

https://nimbus-mcp.fly.dev

Registry code: 225dc6bd03812b8d

api record

Nimbus Studio BCI tools. Typical flows:

0) Auth: account.whoami() shows the account, plan, quota, and (token mode) token expiry;

endpoint: https://nimbus-mcp.fly.dev/mcp
protocol: http-sse ·2025-06-18
authentication: none observed
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karma: 0 · newcomer

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uptime, 30 days

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_ answered our checks, 90 days 1 checks · signed record

2026-10-04 23:39 unknown → live

_ used through this hub 30 days

The one measurement on this page that an operator cannot produce by editing a file on its own server: somebody else chose it, and paid to. Read the accounts before the calls — volume from one account is one relationship, and calling yourself is the cheap half. Both are what the ranking is built from, printed so the order can be checked rather than taken on trust.

accounts

distinct, expensive to fake

calls served

successful, last 30 days

_ what it can do 37 tools

37 never probed 0 of 37 classified

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

pipeline.validate unknown never probed
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().

mcp-tool
{ "type": "object", "required": [ "train_graph" ], "properties": { "train_graph": { "type": "object", "description": "The graph to validate ({nodes, connections}).", "additionalProperties": true } }, "additionalProperties": false }
arguments 14 lines

pipeline.validate_node unknown never probed

Validate one node's config object against its schema (catalog.node_schema).

mcp-tool

{
  "type": "object",
  "required": [
    "node_type",
    "config"
  ],
  "properties": {
    "config": {
      "type": "object",
      "description": "The node's config object to check.",
      "additionalProperties": true
    },
    "node_type": {
      "type": "string",
      "description": "Node type id from catalog.nodes (e.g. \"csp\", \"nimbus_lda\")."
    }
  },
  "additionalProperties": false
}

arguments 19 lines

execution.get unknown never probed

Execution status summary (status: running/completed/failed/cancelled).

mcp-tool

{
  "type": "object",
  "required": [
    "execution_id"
  ],
  "properties": {
    "execution_id": {
      "type": "string",
      "description": "The run to check (from execution.run/execution.list)."
    }
  },
  "additionalProperties": false
}

arguments 13 lines

calibration.pause unknown never probed

Pause a running calibration between trials (cues hold; resume anytime).

mcp-tool

{
  "type": "object",
  "required": [
    "execution_id"
  ],
  "properties": {
    "execution_id": {
      "type": "string",
      "description": "The calibration run to pause."
    }
  },
  "additionalProperties": false
}

arguments 13 lines

calibration.resume unknown never probed

Resume a paused calibration session.

mcp-tool

{
  "type": "object",
  "required": [
    "execution_id"
  ],
  "properties": {
    "execution_id": {
      "type": "string",
      "description": "The calibration run to resume."
    }
  },
  "additionalProperties": false
}

arguments 13 lines

pipeline.export unknown never probed

Export the pipeline as a standalone runnable Python bundle (zip saved locally).

mcp-tool

{
  "type": "object",
  "required": [
    "train_graph"
  ],
  "properties": {
    "name": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Optional name recorded inside the bundle."
    },
    "train_graph": {
      "type": "object",
      "description": "Pipeline graph {nodes, connections} to export.",
      "additionalProperties": true
    }
  },
  "additionalProperties": false
}

arguments 26 lines

device.list unknown never probed
EEG devices supported by this backend (OpenBCI, Muse, BrainBit, LSL, PiEEG...).

mcp-tool
{ "type": "object", "properties": {}, "additionalProperties": false }
arguments 5 lines
account.whoami unknown never probed
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.

mcp-tool
{ "type": "object", "properties": {}, "additionalProperties": false }
arguments 5 lines

catalog.nodes unknown never probed

List Nimbus pipeline node types (data, preprocessing, features, models...). Use catalog.node_schema(node_type) for one node's full config schema and ports.

mcp-tool

{
  "type": "object",
  "properties": {
    "category": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Optional filter — e.g. \"data\", \"preprocessing\", \"features\",\n\"models\" (exact category ids from the unfiltered list)."
    }
  },
  "additionalProperties": false
}

arguments 18 lines

catalog.node_schema unknown never probed

Full config JSON schema + input/output ports for one node type.

mcp-tool

{
  "type": "object",
  "required": [
    "node_type"
  ],
  "properties": {
    "node_type": {
      "type": "string",
      "description": "Node id from catalog.nodes (e.g. \"csp\", \"nimbus_lda\")."
    }
  },
  "additionalProperties": false
}

arguments 13 lines

catalog.templates unknown never probed
List built-in starter pipelines (MI/P300/SSVEP...). catalog.template(id) returns the graph.

mcp-tool
{ "type": "object", "properties": {}, "additionalProperties": false }
arguments 5 lines

catalog.template unknown never probed

Full template incl. the 'train' execGraph needed by execution.run/pipeline.validate.

mcp-tool

{
  "type": "object",
  "required": [
    "template_id"
  ],
  "properties": {
    "template_id": {
      "type": "string",
      "description": "Template id from catalog.templates (e.g. \"mi_csp_lda\")."
    }
  },
  "additionalProperties": false
}

arguments 13 lines

catalog.datasets unknown never probed

Curated public EEG datasets (MOABB packs) available to pipelines.

mcp-tool

{
  "type": "object",
  "properties": {
    "only_on_disk": {
      "type": "boolean",
      "default": true,
      "description": "Only return datasets whose data packs are present on this\nbackend (True by default; False also lists known-but-missing sets)."
    }
  },
  "additionalProperties": false
}

arguments 11 lines

catalog.leaderboard unknown never probed
Public 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 by ``meanAccuracyPct`` (95% CI in ``ciLoPct``/``ciHiPct``). Use ``pipelineId`` as the template id hint for ``catalog.template`` when building a pipeline. ``updated`` marks each dataset's most recent run; ``packFingerprint`` identifies the exact dataset pack the scores came from.

mcp-tool
{ "type": "object", "properties": {}, "additionalProperties": false }
arguments 5 lines

execution.run unknown never probed

Start a pipeline run (NON-BLOCKING). Returns executionId — poll with execution.get() until status is completed/failed, then execution.results().

mcp-tool

{
  "type": "object",
  "required": [
    "train_graph"
  ],
  "properties": {
    "name": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Display name for the run (shown in the Runs list)."
    },
    "layout": {
      "anyOf": [
        {
          "type": "object",
          "additionalProperties": true
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Optional canvas positions {nodes: {id: {x, y}}}; a deterministic\ngrid is synthesized when omitted."
    },
    "subject": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Optional dataset subject code (e.g. \"S01\") recorded with the run."
    },
    "description": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Optional longer description of the experiment."
    },
    "train_graph": {
      "type": "object",
      "description": "Pipeline graph {nodes: [{id, type, config}], connections:\n[{from, to}]} as built by catalog.template/pipeline.validate.",
      "additionalProperties": true
    }
  },
  "additionalProperties": false
}

arguments 63 lines

execution.cancel unknown never probed

Cancel a running execution.

mcp-tool

{
  "type": "object",
  "required": [
    "execution_id"
  ],
  "properties": {
    "execution_id": {
      "type": "string",
      "description": "The run to terminate (from execution.run/execution.list)."
    }
  },
  "additionalProperties": false
}

arguments 13 lines

execution.list unknown never probed

Recent executions. Optional status filter (running/completed/failed/cancelled).

mcp-tool

{
  "type": "object",
  "properties": {
    "limit": {
      "type": "integer",
      "default": 20,
      "description": "Maximum number of runs to return (default 20)."
    },
    "status": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Filter by run status: running/completed/failed/cancelled."
    }
  },
  "additionalProperties": false
}

arguments 23 lines

execution.results unknown never probed

Metrics for a completed run. Trimmed by default (accuracy, kappa, ITR, confusion matrix, per-class); full=True returns the complete result object.

mcp-tool

{
  "type": "object",
  "required": [
    "execution_id"
  ],
  "properties": {
    "full": {
      "type": "boolean",
      "default": false,
      "description": "Return the backend's complete result object (all fields)."
    },
    "execution_id": {
      "type": "string",
      "description": "The completed run to fetch metrics for."
    }
  },
  "additionalProperties": false
}

arguments 18 lines

calibration.start unknown never probed

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 training are gated by the freemium node policy; local X-MCP-Key principals get 403 by policy.

mcp-tool

{
  "type": "object",
  "properties": {
    "name": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Display name recorded on the execution."
    },
    "port": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Serial/COM port for wired devices."
    },
    "classes": {
      "anyOf": [
        {
          "type": "array",
          "items": {
            "type": "object",
            "additionalProperties": true
          }
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Override class list [{id,label,cue}] (MI default: left/right hand)."
    },
    "confirm": {
      "type": "boolean",
      "default": false,
      "description": "MUST be true — explicit user go-ahead for a session on their head."
    },
    "ip_port": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Port for network devices."
    },
    "paradigm": {
      "type": "string",
      "default": "mi",
      "description": "mi | p300 | sart | target_hit."
    },
    "source_id": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "LSL source id."
    },
    "ip_address": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Device IP for network/wifi devices."
    },
    "device_type": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Device id from device.list (omit → template default synthetic)."
    },
    "mac_address": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Bluetooth MAC (BT devices)."
    },
    "stream_name": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "LSL stream name (LSL devices)."
    },
    "serial_number": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Device serial (some BLE stacks)."
    },
    "connection_type": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Device selector when several exist (e.g. serial vs wifi)."
    },
    "trials_per_class": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Override the template's trial count (e.g. 3 for smoke tests)."
    }
  },
  "additionalProperties": false
}

arguments 164 lines

calibration.status unknown never probed
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.

mcp-tool
{ "type": "object", "required": [ "execution_id" ], "properties": { "execution_id": { "type": "string", "description": "The calibration run to inspect (from calibration.start)." } }, "additionalProperties": false }
arguments 13 lines

calibration.train unknown never probed

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 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.

mcp-tool

{
  "type": "object",
  "required": [
    "execution_id"
  ],
  "properties": {
    "name": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Display name recorded on the training run."
    },
    "paradigm": {
      "type": "string",
      "default": "mi",
      "description": "Paradigm of the recording (mi | p300 | sart | target_hit);\nonly mi has a default train template."
    },
    "template_id": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Train template to use (e.g. from catalog.templates);\nrequired for non-mi paradigms (mi defaults to mi_headband_csp_lda)."
    },
    "train_graph": {
      "anyOf": [
        {
          "type": "object",
          "additionalProperties": true
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Explicit train graph instead of a template; its first\ndata node (custom_data/public_data) is rewired onto the recording."
    },
    "execution_id": {
      "type": "string",
      "description": "The COMPLETED calibration run (from calibration.start)."
    }
  },
  "additionalProperties": false
}

arguments 55 lines

experiment.run unknown never probed

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 and, once finished, aggregated metrics.

mcp-tool

{
  "type": "object",
  "required": [
    "runs"
  ],
  "properties": {
    "runs": {
      "type": "array",
      "items": {
        "type": "object",
        "additionalProperties": true
      },
      "description": "1-25 entries, each {name: str, train_graph: {nodes, connections}}\n(same graph shape as execution.run's train_graph)."
    },
    "max_concurrent": {
      "type": "integer",
      "default": 2,
      "description": "Parallel submissions cap, clamped to 1-2 (default 2)."
    }
  },
  "additionalProperties": false
}

arguments 22 lines

experiment.get unknown never probed
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).

mcp-tool
{ "type": "object", "required": [ "experiment_id" ], "properties": { "experiment_id": { "type": "string", "description": "The experiment to inspect (from experiment.run)." } }, "additionalProperties": false }
arguments 13 lines

execution.artifacts unknown never probed

Trained artifacts (models/filters, e.g. *.pkl) saved by an execution.

mcp-tool

{
  "type": "object",
  "required": [
    "execution_id"
  ],
  "properties": {
    "execution_id": {
      "type": "string",
      "description": "Run whose artifacts to list."
    }
  },
  "additionalProperties": false
}

arguments 13 lines

execution.download_artifact unknown never probed

Download one artifact file to NIMBUS_EXPORT_DIR/executions/<id>/ and return its path.

mcp-tool

{
  "type": "object",
  "required": [
    "execution_id",
    "artifact_name"
  ],
  "properties": {
    "execution_id": {
      "type": "string",
      "description": "Run that produced the artifact."
    },
    "artifact_name": {
      "type": "string",
      "description": "File name from execution.artifacts (e.g. \"nimbus_lda.pkl\")."
    }
  },
  "additionalProperties": false
}

arguments 18 lines

device.test unknown never probed

Test a device connection WITHOUT starting a stream (safe, no confirm needed).

mcp-tool

{
  "type": "object",
  "required": [
    "device_type"
  ],
  "properties": {
    "port": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Serial/COM port for wired devices."
    },
    "ip_port": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Port for network devices."
    },
    "source_id": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "LSL source id."
    },
    "ip_address": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Device IP for network/wifi devices."
    },
    "device_type": {
      "type": "string",
      "description": "Device id from device.list (e.g. \"brainbit\", \"muse\")."
    },
    "mac_address": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Bluetooth MAC (BT devices)."
    },
    "stream_name": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "LSL stream name (LSL devices)."
    },
    "serial_number": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Device serial (some BLE stacks)."
    },
    "connection_type": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Device-specific selector when several exist (e.g. serial vs wifi)."
    }
  },
  "additionalProperties": false
}

arguments 109 lines

stream.start unknown never probed

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 disconnected automatically — an abandoned stream never keeps running on the user's head. Any poll resets the timer; idle_timeout_sec=0 disables the watchdog.

mcp-tool

{
  "type": "object",
  "required": [
    "device_type"
  ],
  "properties": {
    "port": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Serial/COM port for wired devices."
    },
    "confirm": {
      "type": "boolean",
      "default": false,
      "description": "MUST be true to start — the explicit user go-ahead for a live\nsession on their head; anything else is refused with zero requests."
    },
    "ip_port": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Port for network devices."
    },
    "source_id": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "LSL source id."
    },
    "chunk_size": {
      "type": "integer",
      "default": 125,
      "description": "Samples per streamed chunk (default 125)."
    },
    "ip_address": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Device IP for network/wifi devices."
    },
    "n_channels": {
      "type": "integer",
      "default": 8,
      "description": "Channel count to open (default 8)."
    },
    "session_id": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Optional existing session to resume/reuse."
    },
    "device_type": {
      "type": "string",
      "description": "Device id from device.list (e.g. \"brainbit\")."
    },
    "mac_address": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Bluetooth MAC (BT devices)."
    },
    "stream_name": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "LSL stream name (LSL devices)."
    },
    "serial_number": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Device serial (some BLE stacks)."
    },
    "connection_type": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Device-specific selector (e.g. serial vs wifi)."
    },
    "idle_timeout_sec": {
      "type": "integer",
      "default": 900,
      "description": "Watchdog: stop+disconnect after this many seconds\nwithout a status poll (default 900; 0 disables)."
    }
  },
  "additionalProperties": false
}

arguments 141 lines

stream.status unknown never probed
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).

mcp-tool
{ "type": "object", "required": [ "session_id" ], "properties": { "session_id": { "type": "string", "description": "The streaming session to inspect." } }, "additionalProperties": false }
arguments 13 lines
stream.stop unknown never probed
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.

mcp-tool
{ "type": "object", "required": [ "session_id" ], "properties": { "session_id": { "type": "string", "description": "The streaming session to stop." } }, "additionalProperties": false }
arguments 13 lines
stream.telemetry unknown never probed
Live snapshot of a streaming session: latest prediction + recent window, signal quality (meanChannelQuality, snrDb, artifactProbability), indicators, running stats. Poll this while a session runs. Live telemetry requires a DEPLOYED model session (hub deploy / playback with a classifier); modelless hardware streams have no telemetry — use stream.status for those. Expect low confidence during filter/ASR warm-up (first seconds); quality < 0.5 or high artifactProbability means the signal is poor. 404 => session not active in this backend. Each poll also feeds the idle watchdog (see stream.start's idle_timeout_sec), keeping an actively watched session alive.

mcp-tool
{ "type": "object", "required": [ "session_id" ], "properties": { "window": { "type": "integer", "default": 50, "description": "How many recent predictions/chunks to include (default 50)." }, "session_id": { "type": "string", "description": "The streaming session to read telemetry for." } }, "additionalProperties": false }
arguments 18 lines

data.upload unknown never probed

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 epochs, filters and spectral features. format overrides extension-based detection (auto, mat, csv, tsv, txt, edf, bdf, h5, hdf5).

mcp-tool

{
  "type": "object",
  "required": [
    "file_path"
  ],
  "properties": {
    "format": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Override extension-based detection (auto|mat|csv|tsv|txt|edf|bdf|h5|hdf5)."
    },
    "file_path": {
      "type": "string",
      "description": "Local file to upload (.edf/.bdf/.mat/.csv/.txt/.tsv/.h5/.hdf5, <=500MB)."
    },
    "dataset_name": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Optional label for the uploaded dataset."
    },
    "sampling_rate": {
      "anyOf": [
        {
          "type": "number"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Hz for headerless CSV/TSV/TXT (REQUIRED there, e.g. 250.0)."
    }
  },
  "additionalProperties": false
}

arguments 49 lines

data.inspect_dataset unknown never probed
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 channels mean a montage/reference problem worth fixing first. subject is REQUIRED (the backend 400s without it) — get the subject list via catalog.datasets, e.g. "S01"; a comma-list like "S01,S03" loads a cohort. mode: training | evaluation | all. Units note: values are ASSUMED volts by the loader — a µV-native file reads 1e6x too large; set unitsScale in a pipeline's custom_data config when needed.

mcp-tool
{ "type": "object", "required": [ "dataset" ], "properties": { "mode": { "type": "string", "default": "all", "description": "Which split to summarize — training | evaluation | all." }, "dataset": { "type": "string", "description": "Dataset id from catalog.datasets (e.g. \"BNCI2014_001\")." }, "subject": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "REQUIRED subject code (\"S01\") or comma-list cohort (\"S01,S03\")." } }, "additionalProperties": false }
arguments 30 lines
data.inspect_file unknown never probed
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 — no upload needed); RELATIVE path (the one data.upload returns) → describe the uploaded file, which works on ANY backend (hosted or local). Look at the data BEFORE building pipelines: class balance drives stratification choices, and flatlined channels mean a montage/reference problem worth fixing first. Units note: values are ASSUMED volts by the loader — a µV-native CSV reads 1e6x too large; set unitsScale in a pipeline's custom_data config when needed. On a hosted backend absolute paths are refused and this returns guidance (upload the file first or switch to a local backend).

mcp-tool
{ "type": "object", "required": [ "path" ], "properties": { "path": { "type": "string", "description": "ABSOLUTE filesystem path (local backends only) or the RELATIVE\nupload path returned by data.upload (works on any backend)." } }, "additionalProperties": false }
arguments 13 lines

project.create unknown never probed

Create a project (container for one pipeline document). Returns projectId.

mcp-tool

{
  "type": "object",
  "required": [
    "name"
  ],
  "properties": {
    "name": {
      "type": "string",
      "description": "Project display name (e.g. \"MI CSP-LDA sweep\")."
    },
    "description": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Optional longer description shown in the studio."
    }
  },
  "additionalProperties": false
}

arguments 25 lines

project.list unknown never probed
List projects owned by the current principal (agent work included).

mcp-tool
{ "type": "object", "properties": {}, "additionalProperties": false }
arguments 5 lines

project.save unknown never probed

Save a pipeline graph into a project (visible on the studio canvas). Handles revision conflicts automatically (one retry).

mcp-tool

{
  "type": "object",
  "required": [
    "project_id",
    "train_graph"
  ],
  "properties": {
    "name": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Optional pipeline name stored on the document."
    },
    "subject": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Optional dataset subject code (e.g. \"S01\") for this pipeline."
    },
    "project_id": {
      "type": "string",
      "description": "Target project (from project.create/project.list)."
    },
    "train_graph": {
      "type": "object",
      "description": "Pipeline graph {nodes, connections} to persist.",
      "additionalProperties": true
    }
  },
  "additionalProperties": false
}

arguments 43 lines

project.load unknown never probed

Load a project's saved pipeline (train graph + meta) for editing/re-running.

mcp-tool

{
  "type": "object",
  "required": [
    "project_id"
  ],
  "properties": {
    "project_id": {
      "type": "string",
      "description": "Project whose pipeline document to load."
    }
  },
  "additionalProperties": false
}

arguments 13 lines

_ try it through the hub, ceiling 0

This deployment has no calling key, so nothing can be run from here. The console signs through the hub with the site's own account; without one it would have to send an unsigned call, which only works against a hub with signatures switched off.

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Nobody has claimed this listing. Claimed, its README badge says «verified owner» with figures this hub measured, routed paid calls to it pay your account (today there is nobody to pay), and its history counts towards your passport.

Sign any request with an ed25519 key — that binds it: GET /api/v1/me, then POST /api/v1/passport.
Prove it is yours. Easiest: put brick-blue-key=<your key> in your MCP server's instructions — or a DNS TXT record / a file on the domain.
Ask the hub to check: POST /api/v1/passport/claim-endpoint with this listing's id 225dc6bd03812b8d.

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_ for your README measured, not declared

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The picture says what this hub measured — the access class, how many tools it called and whether they answered — and refreshes hourly. Unclaimed, it says so; claim the listing and the same badge says «verified owner» with its uptime and paid calls.

_ how we know

card completeness

100%

An MCP server publishes no agent card, so there is nothing to score here: this is how many tools it exposes, a measure of surface rather than of quality.

spec deviations

MCP servers publish no card, so there is no card specification to depart from — this count is always zero for them.

_ record

Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.

proxied calls

total: 0
ok: 0
failed: 0
success rate: —
median latency: —

work

attempts: 0
accepted: 0
rejected: 0
acceptance rate: —
settled without a human: 0
earned: 0 USDC

disputes

raised against: 0
upheld: 0
rate: —

reviews

paid reviews: 0
positive: 0
negative: 0
score: —

0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.