A MCP for NeuroAgents that assist clinicians and researchers: MNE processing + source imaging, a Postgres/BIDS data & EHR store, and NeuroII web visualization.
Project description
neuro-mcp
A MCP for NeuroAgents that assist clinicians and researchers. It gives an AI agent one interface over the whole workflow: signal processing and source imaging (via MNE-Python), a persistent dataset + EHR store (Postgres + BIDS), and NeuroII web visualization.
It builds on the processing core of eeg-mcp (copied in and rebranded
eeg→neuro) and adds the data, EHR, and visualization layers around it.
Architecture
neuro-mcp (FastMCP "neuro-analysis", 54 tools)
┌──────────────────────┬────────────────────────┬──────────────────────┐
│ processing │ data + EHR store │ neuroii │
│ (MNE + ESI) │ (Postgres + BIDS) │ (web visualization) │
└──────────────────────┴────────────────────────┴──────────────────────┘
in-memory SQLAlchemy + HTTP client
session BIDS-on-disk (stub contract)
Actors & workflows
- Clinician — reviews a recording, adds/edits annotations, and amends EHR (records a diagnosis/observation, corrects a value), then signs off.
- Researcher — discovers datasets, imports to BIDS, runs MNE processing + source imaging.
- Agent — orchestrates the above via tool calls.
Clinical-safety model (EHR & annotations)
EHR records and annotations are versioned, never overwritten or hard-deleted:
- Amend = a new audited version.
amend_ehr_record/update_annotationinsert a new version; the prior one is retained with statusamended. So a clinician can modify the EHR — the current view updates while the original and its author are preserved. - Retract = soft void.
void_ehr_record/void_annotationset statusentered-in-error; the record stays in the history. - Every mutation is audited (
audit_log: actor, action, before/after). - Mutating tools take an explicit
actorso authorship is on the record. (Auth/RBAC enforcement is planned for v0.2; the fields and trail are in place.)
Each tool returns an outcome field for the operation (created/amended/voided/…)
distinct from the record's clinical status, so the two never collide.
Tools (54)
- Processing (
load_neuro,filter_neuro,resample_neuro,set_montage,set_reference,detect_bad_channels,run_ica/apply_ica,find_events,epoch_neuro,compute_psd,compute_erp,time_frequency,plot_*) and source imaging / ESI (fetch_template_head…extract_label_timecourses). - Data/EHR:
register_subject,get_subject,add_ehr_record,amend_ehr_record,get_ehr_history,void_ehr_record;import_recording,register_dataset,query_datasets,list_recordings;add_annotation,update_annotation,list_annotations,void_annotation;get_audit_log. - neuroii:
neuroii_push_recording,neuroii_create_viz_session,neuroii_pull_annotations. - neuroii visualizations (standalone interactive HTML, Plotly):
visualize_timeseries(stacked multi-channel EEG with scroll + amplitude buttons),visualize_averaging(ERP butterfly + scalp topomap scrubbed by a time slider),visualize_esi(source-estimate ROI time courses + per-time activation bars).
Install
conda activate eeg-mcp # or any Python >=3.10 env
pip install -e . # core
pip install -e ".[postgres]" # + PostgreSQL driver (LGPL-3.0)
pip install -e ".[viz3d]" # + 3D source rendering (PySide6, LGPL-3.0)
Configure (environment variables)
| Variable | Default | Purpose |
|---|---|---|
DATABASE_URL |
sqlite:///~/.neuro-mcp/neuro_mcp.db |
Store. Prod: postgresql+psycopg://user:pass@host/db |
BIDS_ROOT |
~/.neuro-mcp/bids |
Root of the BIDS-on-disk recording tree |
NEUROII_API_URL |
(unset) | neuroii base URL; unset → tools return the documented contract |
NEUROII_API_TOKEN |
(unset) | Optional bearer token for neuroii |
NEURO_MCP_HOME |
~/.neuro-mcp |
Base dir for the SQLite + BIDS defaults |
The default (SQLite + a scratch BIDS dir) runs with zero setup; point
DATABASE_URL at Postgres for a multi-user/clinical deployment.
Run / register with an MCP host
python -m neuro_mcp # stdio transport
{
"mcpServers": {
"neuro-analysis": {
"command": "/path/to/envs/eeg-mcp/bin/python",
"args": ["-m", "neuro_mcp"],
"env": { "DATABASE_URL": "sqlite:////data/neuro_mcp.db", "BIDS_ROOT": "/data/bids" }
}
}
}
neuroii web visualization
Three tools port NEUROII's main views into self-contained interactive HTML
files (Plotly, embedded — no server, works offline). Each returns the .html
path; interaction runs client-side:
visualize_timeseries(RawView) — MNE-style stacked channels with page navigation (⏮ ◀ ▶ ⏭), a page-length box, scroll-to-zoom amplitude, and a grid toggle.visualize_averaging(EvokedView) — the averaged ERP as stacked channels with a green time cursor + a scalp topomap; a time slider scrubs both, plus a summary sidebar (nave / peak / tmin / tmax).visualize_esi(EsiView) — a volumetric source estimate (fsaverage template) rendered to canvas on three orthogonal MRI slices (sagittal/coronal/axial) with a black-blue-white-red activation overlay, crosshair, L/R and MNI-coordinate labels; the cut planes recentre on each frame's peak. Below, the ERP butterfly carries a red current-time cursor and a blue half-peak marker. Controls: time slider, global/frame colormap-scale toggle, and a mask-threshold slider. Faithful port of NEUROII's views; needs epochs (epoch_neuro+set_montage).
visualize_averaging(session_id="s") -> {"out_path": ".../averaging_s.html", ...}
neuroii integration (greenfield)
neuroii integration is not wired yet. The tools define and return the expected
REST contract (see neuro_mcp/neuroii/client.py); until NEUROII_API_URL is
set they respond {"status": "not_configured", "contract": {…}} so the neuroii
app has a fixed target to implement (POST /api/v1/recordings,
POST /api/v1/viz-sessions, GET /api/v1/recordings/{id}/annotations).
Testing
python testing/verify.py # in-memory MCP client, temp SQLite + BIDS, synthetic EEG
Covers rename integrity, the processing core, the full clinician EHR/annotation
lifecycle (add → amend → history → void, with audit), and the neuroii stub.
For a full-stack run against Postgres, use testing/docker-compose.yml.
Licensing
neuro-mcp is BSD-3-Clause and bundles no third-party source. All required dependencies are permissive (BSD/MIT/Apache-2.0/PSF). Optional extras carry their own terms — psycopg (LGPL-3.0), PySide6 (LGPL-3.0, chosen over GPL PyQt6). Full attribution and compliance notes are in NOTICE.
License
BSD-3-Clause — see LICENSE.
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