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BrainFlow Biosensing Boards

PYPI · LABMCP-BRAINFLOW · SCANNED SEP 30

MCP server for EEG/EMG/ECG/PPG boards via BrainFlow (OpenBCI, Muse, Neurosity, Unicorn, BrainBit).

Available components

65 Trust /100
Trust breakdown (7 categories)

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →

Supply Chain Security49
  • Malware scan not yet available for this package.Unverified
  • No known CVEs affecting this package version or its production dependencies.Pass
  • Runs hatchling.build at install time, a recognised build step with no custom scripting around it. View diagnostics → Pass
  • 2 of 20 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency100
  • Source repository is publicly reachable at the declared URL. View diagnostics → Pass
  • Cryptographically verified build provenance (signed, bound to K-Dense-AI/lab-instrument-mcps). View diagnostics → Pass
  • Clear OSI-approved license (Apache-2.0).Pass
  • Actively maintained (last published 3 days ago).Pass
  • Publishes a security disclosure policy (SECURITY.md).Pass
Schema Quality & AI Usability73
  • AI-judged instruction clarity (good).Pass
  • Context-footprint check failed: tool/resource definitions use about 1477 tokens (~123/item across 12 items; 12 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
  • Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management0
  • Stability not yet verified: not enough scan history yet (needs a 30-day window).Unverified
Tool Coverage93
  • 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
  • 76% of tool parameters carry a description.Partial
  • Structured output schemas are declared (100% of tools); any adoption earns full credit.Pass
Tool Safety100
  • No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
  • All 1 tool(s) whose name or description implies an irreversible operation declare an MCP destructiveHint annotation.Pass
  • An AI judge read all 13 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
  • Implements a current MCP spec version (2026-07-28).Pass

Unverified: 1 category

A category scored 0 because we could not verify it: a data source with nothing on this package, evidence we could not reach, or a check we could not run. We only credit what we can confirm.

Install

How do I install the BrainFlow Biosensing Boards MCP server?

BrainFlow Biosensing Boards runs locally as a PyPI package, launched with uvx labmcp-brainflow. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.

pypi · labmcp-brainflow

# add to Claude Code
claude mcp add k-dense-ai-labmcp-brainflow -- uvx labmcp-brainflow
// .cursor/mcp.json
{
  "mcpServers": {
    "k-dense-ai-labmcp-brainflow": {
      "command": "uvx",
      "args": [
        "labmcp-brainflow"
      ]
    }
  }
}
// .vscode/mcp.json
{
  "servers": {
    "k-dense-ai-labmcp-brainflow": {
      "command": "uvx",
      "args": [
        "labmcp-brainflow"
      ]
    }
  }
}
# add to Codex CLI
codex mcp add k-dense-ai-labmcp-brainflow -- uvx labmcp-brainflow
// opencode.json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "k-dense-ai-labmcp-brainflow": {
      "type": "local",
      "command": [
        "uvx",
        "labmcp-brainflow"
      ],
      "enabled": true
    }
  }
}
# add to OpenClaw
openclaw mcp add k-dense-ai-labmcp-brainflow --command uvx --arg labmcp-brainflow
# ~/.hermes/config.yaml
mcp_servers:
  k-dense-ai-labmcp-brainflow:
    command: "uvx"
    args: ["labmcp-brainflow"]
// ~/.netclaw/config/netclaw.json
{
  "McpServers": {
    "k-dense-ai-labmcp-brainflow": {
      "Transport": "stdio",
      "Command": "uvx",
      "Arguments": [
        "labmcp-brainflow"
      ]
    }
  }
}
# add to Vellum
assistant mcp add k-dense-ai-labmcp-brainflow -t stdio -c uvx -a labmcp-brainflow
// mcp.json
{
  "mcpServers": {
    "k-dense-ai-labmcp-brainflow": {
      "command": "uvx",
      "args": [
        "labmcp-brainflow"
      ]
    }
  }
}
Changelog

Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.

  • 28 Sept 26 −5
    • We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
  • 27 Sept 26 0
    • Package version: 0.1.1 → 0.1.2 functional
  • 26 Sept 26 70

    First indexed and scored.

Diagnostics

Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.

Captured 30 Sept 2026 · Analysed pypi/labmcp-brainflow@0.1.2

Provenance Verified

A signed build attestation was found and verified, binding this exact artifact to the source repository it claims to come from.

Result Verified
Ecosystem pypi
Reason Verified
Discovered via Registry attestation endpoint
Source repo K-Dense-AI/lab-instrument-mcps
Certificate issuer https://token.actions.githubusercontent.com
Certificate SAN https://github.com/K-Dense-AI/lab-instrument-mcps/.github/workflows/release.yml@refs/tags/labmcp-brainflow-v0.1.2
Rekor log index 2969518162
Predicate type PyPI publish attestation https://docs.pypi.org/attestations/publish/v1
Subject digest sha256:cc6803d50bf892298a2196f6922b7f53dc2686d433e90765accb0ac8d0820449

Background: How many MCP packages publish verified provenance →

Install scripts 1 script
Hook Tier Command
build_backend allowlisted hatchling.build

Background: Why install scripts are a supply-chain risk →

Dependencies 20 packages
Packages resolved 20
Stale 2
Tree resolution Complete

Background: SBOMs and build attestations, explained →

MCP tools · 12 exposed · ~1,032 tokens

The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →

Tool Tokens
configure_board ~149

Send a raw board-specific command to the firmware through BrainFlow's config_board (e.g. OpenBCI channel settings 'x1060110X', test signals, or Muse presets 'p50'/'p61' to enable PPG). The string is passed through unchecked: besides acquisition settings, some commands switch on outputs such as the lead-off (impedance-test) current that flows through the participant's electrodes. Settings persist until another command changes them or the board is power-cycled (stopping the stream does not undo them). Consult the board's SDK documentation first and tell the user what the command does.

NameTypeReqDescription
commandstringyesBoard-specific configuration string
NameTypeReqDescription
commandstringyes–
replystringyes–
timestampstringyes–

No examples provided.

get_band_powers ~122

EEG band powers (delta 1-4, theta 4-8, alpha 8-13, beta 13-30, gamma 30-50 Hz) over the most recent `window_s` seconds: BrainFlow's channel-averaged relative powers plus per-channel absolute (uV^2) and relative powers and the peak frequency. Records a fresh window if not streaming.

NameTypeReqDescription
channel_typestring–EXG channel group to analyse
window_snumber–Seconds of data to analyse (4 s recommended)
NameTypeReqDescription
average_relativeobjectyesBrainFlow DataFilter.get_avg_band_powers: channel-averaged, relative (sums to 1)
average_relative_stddevobjectyesAcross-channel stddev / mean per band
bands_hzobjectyes–
boardstringyes–
channelsarrayyes–
processingstringyes–
sampling_rate_hzintegeryes–
simulatedbooleanyes–
timestampstringyes–
window_snumberyes–

No examples provided.

get_board_info ~59

Describe the connected board: channel names by type (EEG/EMG/ECG/EOG share the EXG rows on most boards), sampling rate, available presets (data buffers) and streaming state.

NameTypeReqDescription
presetstring––
NameTypeReqDescription
backendstringyesBrainFlow version, or the built-in fake board
boardstringyes–
board_idintegeryes–
buffered_samplesintegeryes–
channelsobjectyesChannel names per type (EXG rows are shared by eeg/emg/ecg/eog)
device_name–yes–
exg_unit–yes–
presetstringyes–
presetsarrayyesData buffers this board provides
sampling_rate_hzintegeryes–
simulatedbooleanyes–
streamingbooleanyes–
timestampstringyes–

No examples provided.

get_command_log ~46

Return the most recent raw commands sent to / replies received from the instrument (newest last). Useful for debugging and for recording what was done.

NameTypeReqDescription
limitinteger––
NameTypeReqDescription
resultarrayyes–

No examples provided.

get_connection_info ~40

Report which instrument is connected (identity, address, simulated or real), whether the server is read-only, and the active safety limits. Call this first.

Input schema present but exposes no named parameters.

Structured output declared, but exposes no named fields.

No examples provided.

get_signal_quality ~83

Check every EXG channel for common electrode problems: flat line (disconnected), railed (amplifier saturated, OpenBCI Cyton boards), strong 50/60 Hz mains noise (poor contact or missing reference), and implausibly high amplitude (movement, muscle, loose electrode).

NameTypeReqDescription
window_snumber–Seconds of data to assess
NameTypeReqDescription
advicestringyes–
boardstringyes–
channelsarrayyes–
dominant_mains_hz–yes–
sampling_rate_hzintegeryes–
simulatedbooleanyes–
summaryobjectyes–
timestampstringyes–
window_snumberyes–

No examples provided.

insert_marker ~80

Write an event marker into the data stream at the current sample (for event-related experiments: stimulus onsets, condition changes). Requires `start_streaming`; markers appear in `record` results and saved files.

NameTypeReqDescription
presetstring––
valuenumberyesEvent code written to the marker channel; must not be 0
NameTypeReqDescription
messagestringyes–
timestampstringyes–
valuenumberyes–

No examples provided.

list_supported_boards ~73

List common BrainFlow boards: the `--option board=` alias, BrainFlow board id, and which connection detail `--address` must hold (serial port, Bluetooth MAC, IP address or serial number). Any other BrainFlow BoardIds name or numeric id is accepted too. Does not need a board.

Input schema present but exposes no named parameters.

NameTypeReqDescription
resultarrayyes–

No examples provided.

reconnect ~35

Close and re-open the connection to the instrument (e.g. after it was power cycled or a cable was re-plugged).

Input schema present but exposes no named parameters.

Structured output declared, but exposes no named fields.

No examples provided.

record ~206

Record `duration_s` seconds and return per-channel statistics, event markers and downsampled traces. Uses the live stream if one is running, otherwise starts a temporary one. The full data (every row, full sampling rate) can be written to `save_path`. `stop_streaming` ends a recording early.

NameTypeReqDescription
channel_typestring–Which channels to summarise
duration_snumber–Seconds of data to collect
include_tracesboolean–Return downsampled traces
max_pointsinteger–Max points per downsampled trace
presetstring––
remove_dcboolean–Subtract each channel's mean from the traces
save_formatstring–csv: labelled columns; brainflow: DataFilter.write_file format (replayable)
save_path––Write the full-resolution data (all rows) to this new file (.csv; never overwrites a file)
NameTypeReqDescription
boardstringyes–
channelsarrayyes–
duration_snumberyes–
end_time–yes–
markersarrayyes–
n_samplesintegeryes–
presetstringyes–
sampling_rate_hzintegeryes–
saved_format–––
saved_to–––
simulatedbooleanyes–
start_time–yes–
stopped_earlyboolean–True if stop_streaming ended the recording early: the data covers only the time before it
timestampstringyes–
trace_times_s––Time axis of the downsampled traces
traces––Downsampled traces per channel
traces_dc_removedbooleanyes–

No examples provided.

start_streaming ~83

Start continuous acquisition into BrainFlow's ring buffer (the board's radio/LEDs switch on; nothing is applied to the participant). Needed for `insert_marker`; `record` then reads from the live stream. Call `stop_streaming` when finished.

NameTypeReqDescription
buffer_duration_snumber–Size of BrainFlow's ring buffer, in seconds of data
NameTypeReqDescription
buffer_duration_s–––
buffered_samplesintegeryes–
messagestringyes–
sampling_rate_hzintegeryes–
streamingbooleanyes–
timestampstringyes–

No examples provided.

stop_streaming ~56

Stop acquisition (saves battery). A `record` in progress ends at once with the data acquired so far. Data already in the buffer is kept until the next stream starts or the session is released (`reconnect`).

Input schema present but exposes no named parameters.

NameTypeReqDescription
buffer_duration_s–––
buffered_samplesintegeryes–
messagestringyes–
sampling_rate_hzintegeryes–
streamingbooleanyes–
timestampstringyes–

No examples provided.

Common questions

What is the BrainFlow Biosensing Boards MCP server?

BrainFlow Biosensing Boards is an MCP server listed in the public MCP registry as io.github.K-Dense-AI/labmcp-brainflow. MCP server for EEG/EMG/ECG/PPG boards via BrainFlow (OpenBCI, Muse, Neurosity, Unicorn, BrainBit). This page covers its PyPI package (labmcp-brainflow).

Is the BrainFlow Biosensing Boards MCP server safe to use?

BrainFlow Biosensing Boards scores 65 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 30 September 2026. Its build provenance is signed and verified. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.

What tools does the BrainFlow Biosensing Boards MCP server expose?

BrainFlow Biosensing Boards exposes 12 tools: get_connection_info, get_command_log, reconnect, list_supported_boards, get_board_info, and 7 more. Their descriptions and schemas cost roughly 1,032 tokens of context every time the server is loaded.

Is the BrainFlow Biosensing Boards MCP server still maintained?

BrainFlow Biosensing Boards is still listed as active in the MCP registry. We last reached this channel on 30 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.

What licence is the BrainFlow Biosensing Boards MCP server under?

BrainFlow Biosensing Boards declares the Apache-2.0 licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.