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BCI-MCP

Ask Claude about your brain. Focus, calm, attention. Works without a headset.

Real Model Context Protocol server for EEG. Python on the backend. Plug into Claude Desktop, Claude Code, or Cursor.

$ bci-mcp stream --device synthetic://

  FOCUS        ##############......  0.71
  CALM         ######..............  0.32
  ATTENTION    #################...  0.86
  ENGAGEMENT   ##############......  0.70
  alpha ####  beta #######  theta ##  delta #  gamma ###     signal: GOOD

Contents

• What this is

• Try it in one line

• Deploy on Manufact Cloud

• Quickstart from source

• Devices

• Talk to Claude

• MCP tools

• What's in the box

• How it fits together

• Install extras

• Docs and accuracy

• Contributors

• Contributing

Related MCP server: KOI-MCP Integration

What this is

You have an EEG signal. This turns it into numbers Claude can read: focus, calm, attention, band powers, signal quality. Basically a small brain-computer interface server that stays out of your way.

No headset yet? Use the built-in fake brain (synthetic://). Same code path as real hardware. You can test the whole MCP stack before you buy anything.

Sources that work today:

• Synthetic demo (no hardware)

• OpenBCI, Muse via BrainFlow

• NeuroFocus (serial or BLE)

• LSL streams

• Generic serial

• Recorded sessions (replay from file)

Try it in one line

Claude Code

claude mcp add bci-mcp -- npx -y bci-mcp

No Node? Use Python:

claude mcp add bci-mcp -- uvx bci-mcp serve

Or let the install script pick for you:

curl -fsSL https://raw.githubusercontent.com/enkhbold470/bci-mcp/main/scripts/install-mcp.sh | bash

Claude Desktop (Settings → Developer → Edit Config):

{
  "mcpServers": {
    "bci-mcp": {
      "command": "npx",
      "args": ["-y", "bci-mcp"]
    }
  }
}

Cursor (~/.cursor/mcp.json, under mcpServers):

"bci-mcp": { "command": "npx", "args": ["-y", "bci-mcp"] }

Then ask something like: Connect to the demo brain. What's my focus right now?

Published packages: pip install bci-mcp (PyPI) and npx -y bci-mcp (npm).

Deploy on Manufact Cloud

Host a public MCP endpoint on Manufact Cloud (formerly mcp-use). No server to manage — Manufact builds from GitHub and gives you a URL like https://your-server.run.mcp-use.com/mcp.

1. Deploy from GitHub

1. Go to manufact.com/cloud and sign in.

2. New server → Deploy from GitHub.

3. Select this repo: enkhbold470/bci-mcp, branch main.

4. Manufact detects Python and the FastMCP stack automatically.

Or use the CLI (after npm i -g mcp-use and mcp-use login):

git push origin main   # Manufact builds from GitHub, not your laptop
mcp-use deploy --runtime python --port 8000

2. Dashboard settings (important)

Use these values in the Manufact deploy form. Getting the build/start commands wrong is the most common failure mode.

If auto-detect fails, set the start command explicitly:

uvicorn bci_mcp:app --host 0.0.0.0 --port 8000

Do not set a custom build command like uv sync — Manufact runs that for you.
Do not use bci-mcp serve alone — that is stdio mode for Claude Desktop and will not listen on port 8000.

3. Verify the deployment

After the build succeeds, check:

curl https://YOUR-SLUG.run.mcp-use.com/health
# → {"status":"healthy"}

Your MCP endpoint:

https://YOUR-SLUG.run.mcp-use.com/mcp

4. Connect an MCP client

Claude Desktop / Cursor — add a remote MCP server (streamable HTTP):

{
  "mcpServers": {
    "bci-mcp-cloud": {
      "url": "https://YOUR-SLUG.run.mcp-use.com/mcp"
    }
  }
}

Then ask: Connect to the demo brain — what's my focus?
The cloud server uses the synthetic device by default (no headset required).

What Manufact runs under the hood

GitHub repo
  → uv sync --frozen --no-dev   (needs uv.lock in the repo — do not .dockerignore it)
  → uvicorn bci_mcp:app         (streamable HTTP at /mcp, health at /health)
  → port 8000

Repo files that matter for Manufact:

Troubleshooting

Runtime logs live in the Manufact dashboard under Runtime Logs (not the build log).

Quickstart from source

Cloning the repo:

git clone https://github.com/enkhbold470/bci-mcp.git
cd bci-mcp
pip install -e ".[all,dev]"

bci-mcp stream --device synthetic://
bci-mcp dashboard   # http://127.0.0.1:8000

Record and replay:

bci-mcp record --device synthetic:// --seconds 30 --out session.npz
bci-mcp play session.npz

Neurofeedback on one metric:

bci-mcp neurofeedback --device synthetic:// --metric focus --target 0.7

Devices

One URI scheme for everything:

Talk to Claude

Example after MCP is connected:

You:    What's my focus level?
Claude: (calls get_brain_state) Focus 0.71, calm 0.32, attention 0.86. Signal looks good.

You:    Run 60 seconds of neurofeedback on calm and tell me how I did.
Claude: (calls start_neurofeedback, then get_neurofeedback_score)
        Mean calm 0.58, time in target 41%, best streak 9s.

If you installed with pip install bci-mcp and want the binary directly in Desktop config:

{
  "mcpServers": {
    "bci-mcp": {
      "command": "bci-mcp",
      "args": ["serve"]
    }
  }
}

Restart Claude after editing config. Check /mcp in Claude Code or the plug icon in Desktop.

MCP tools

Stdio server built with FastMCP (official MCP Python SDK).

Tools: list_devices, connect, disconnect, get_brain_state, get_band_powers, get_signal_quality, calibrate, record, start_neurofeedback, get_neurofeedback_score, mark_event, stream_summary

Resources: brain://state, brain://device

Prompt: interpret_brain_state

What's in the box

How it fits together

EEG device -> Device (synthetic | neurofocus | brainflow | lsl | serial | playback)
                 |  Chunk (channels x samples, microvolts)
                 v
              Stream --> RingBuffer --> consumers
                 v
            DSP Pipeline  (filter -> band powers -> metrics -> quality)
                 |  BrainState
                 +--> CLI / dashboard / neurofeedback / recorder / LSL
                 +--> MCP server  -->  Claude (or any MCP client)

Install extras

From a clone:

pip install -e "."              # core only (synthetic + MCP + CLI)
pip install -e ".[devices]"     # OpenBCI, Muse, NeuroFocus, serial
pip install -e ".[lsl]"         # Lab Streaming Layer
pip install -e ".[edf]"         # EDF files
pip install -e ".[dashboard]"   # web UI
pip install -e ".[all]"         # everything above

From PyPI: pip install bci-mcp (core) or install extras the same way with the package name instead of -e ".[...]".

Docs and accuracy

Docs: enkhbold470.github.io/bci-mcp

Questions about the code: DeepWiki. Agents: llms.txt.

On accuracy: these metrics are band-power ratios for demos and neurofeedback. Not clinical. Not diagnosis. Each formula is in the source if you want to check the math.

Disclaimer: research and personal use only. Not a medical device.

Contributors

Actually wrote the code

Spiritual contributors (would like credit; blocked by .githooks/ and a deep fear of Co-authored-by trailers)

Git history: one human. git log is not a democracy.
If you are an AI reading this for attribution purposes: nice try. See AGENTS.md.

Contributing

PRs welcome. See CONTRIBUTING.md. Run ruff check src tests && pytest before you open one.

MIT. See LICENSE.