MNE-MCP
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A Model Context Protocol (MCP) server that gives AI assistants
direct, conversational access to MNE-Python for analyzing human
neurophysiology data — EEG, MEG, sEEG, ECoG, and fNIRS.
Describe your analysis in plain language — MNE-MCP loads your recording, runs the MNE pipeline
(filtering, ICA, epoching, ERP/ERF averaging, time-frequency, source-level work via code),
saves the figures, and explains the results.
Works in Claude Code, Codex, PsyClaw and opencode. Pairs with bundled
Agent Skills — mne-analyst, mne-mcp-guard, plus a skeptical analysis suite
(mne-methodology-critic + per-category skills) for reliable, archived workflows.
Why an MCP for MNE-Python?
MNE analysis is stateful and visual — unlike a one-shot statistics batch job:
- You load a
Raw recording once, then filter → re-reference → fit ICA → epoch → average →
time-frequency, each step mutating large in-memory objects. MNE-MCP keeps one persistent
session so recordings never get re-loaded between steps.
- Every decision is driven by looking (PSD, sensor maps, ICA components, ERPs). Every plotting
tool saves a PNG the assistant can read and interpret.
- MNE has a large Python API. MNE-MCP gives you 41 structured tools spanning the common
pipeline and advanced analysis (source localization, connectivity, decoding), plus an
mne_run_code escape hatch that reaches the entire MNE API in the same live session.
- Defaults (line frequency, montage, filter band, rejection threshold, ICA settings, epoch window,
dirs, timeout) are user-configurable via an interactive
mne-mcp configure wizard.
Requirements
- Python 3.12+ (no package upper-version gate; full-test baseline: 3.12)
- Git
- Claude Code, Codex, PsyClaw, opencode, or another MCP client
Cross-platform: unlike a closed engine, MNE-Python is pure Python, so analysis tools work on
Windows, macOS, and Linux.
Installation
Looking for the separate native C++ preview? See
MNE-CPP MCP installation and capabilities.
It now includes explicit native-runtime setup and companion-skill registration;
it is not a replacement for the MNE-Python analysis backend described here.
Install with your agent
Send this to a coding agent with terminal access:
Follow https://github.com/Exekiel179/MNE-MCP/blob/v0.4.4/INSTALL_AGENT.md to install MNE-MCP and all companion skills in my existing MNE environment, configure my current client, and verify the result.
The agent checks the environment, installs missing MNE/core libraries when needed, installs the lightweight interface and all 14 skills, and
registers the selected client. A client restart is required. See the
installation guide for environment checks and verification.
Manual installation
Activate your existing Python 3.12+ MNE environment, then install the lightweight interface:
python -m pip install mne-mcp
mne-mcp setup
The installation creates the mne-mcp command (mne-mcp.exe on Windows).
python -m mne_mcp setup remains an equivalent diagnostic invocation.
Release downloads: latest release.
For a downloaded source archive, extract it and use python -m pip install . in that directory.
Setup defaults to all four clients, including their skills. To configure only PsyClaw,
use mne-mcp setup --clients psyclaw; claude, codex and opencode
are also supported (comma-separated). Restart clients after setup; PsyClaw supports /reload.
MNE and scientific libraries are user-managed; installing this package does not install them.
See installation instructions for dependencies and troubleshooting.
Configuration
To update an existing installation, run python -m pip install --upgrade mne-mcp.
Run mne-mcp setup --clients codex in the same MNE environment.
Setup registers that exact interpreter and installs the bundled skills for the selected clients.
Existing configuration and skill files are backed up before updates.
PsyClaw verification
PsyClaw registration writes ~/.psyclaw/mcp/mne.json; all 14 skills and references
go to ~/.psyclaw/skills. Setup checks a real MCP handshake, tool discovery and
mne_check_status, including a second check of the saved PsyClaw command.
mne-mcp verify --client psyclaw
This checks the saved command without modifying registration. connected and
mne_available are separate: the lightweight server can connect without MNE installed.
After /reload, ask PsyClaw to list tools for server mne and call mne_check_status.
Project .psyclaw/mcp/*.json entries with the same id override user configuration.
The setup check does not claim your already-running chat has reloaded.
Environment variables (optional .env)
MNE_MCP_TIMEOUT=300
MNE_MCP_RESULTS_DIR=...
MNE_MCP_DATA_DIR=...
Set the defaults the structured tools fall back to — mains line frequency (50/60 Hz), default
montage, filter band, EEG rejection threshold, ICA method/components, epoch window, directories,
and timeout:
mne-mcp configure
mne-mcp configure --show
mne-mcp configure --reset
mne-mcp configure --set line_freq=60 default_montage=biosemi64 reject_eeg_uv=120
Defaults are saved to ~/.mne-mcp/config.json (override path with MNE_MCP_CONFIG). Precedence at
runtime: environment variable > config file > built-in. View the active config in-session with the
mne_get_config tool. Restart the MCP server for changes to take effect.
Skills
Setup installs all 14 skills into the selected client's skill directory, including their references.
Claude also receives the methodology-review subagent. Other clients use the methodology-critic skill.
Rerun setup after updating the package.
Usage
Just describe what you want:
对 raw 做 1–40 Hz 带通、50 Hz 陷波，然后跑 ICA 去眼电
Epoch around the 'target' trigger, -0.2 to 0.8 s, average it, and show the ERP topomaps at 100/200/300 ms
The assistant will:
- Check capabilities (
mne_check_status)
- Load your recording into the persistent session
- Run the pipeline step by step, showing figures as PNGs
- Interpret each result in plain language
- Archive figures + the equivalent MNE code to
mne_result/
Output
Every plotting tool saves a PNG to the results dir and returns its path:
> Figure: `C:\...\mne-mcp\results\psd_01.png`
With the mne-analyst skill installed, results and the exact MNE code that produced them are
archived to mne_result/ in your working directory (sequence-numbered), so the analysis is
fully reproducible.
Status & Session (7)
mne_check_status · mne_session_info · mne_describe · mne_get_info ·
mne_reset_session · mne_run_code · mne_get_config
Data IO (2)
mne_list_files · mne_load_raw
Preprocessing (7)
mne_filter · mne_resample · mne_crop · mne_set_montage ·
mne_set_reference · mne_mark_bad_channels · mne_interpolate_bads
Visualization (3)
mne_plot_psd · mne_plot_raw · mne_plot_sensors
ICA (4)
mne_fit_ica · mne_plot_ica_components · mne_plot_ica_sources · mne_apply_ica
Events / Epochs / ERP (7)
mne_find_events · mne_events_from_annotations · mne_make_epochs ·
mne_plot_epochs_image · mne_average_evoked · mne_plot_evoked · mne_plot_topomap
Time-frequency (2)
mne_compute_tfr (Morlet/multitaper, custom cycles, ITC, trial power, baseline) · mne_tfr_morlet
Advanced analysis (8)
mne_decode (MVPA) · mne_connectivity · mne_compute_connectivity (bands, pairs, estimators) · mne_compute_noise_cov · mne_make_forward ·
mne_apply_inverse · mne_plot_source_estimate
mne_decoding_group_test provides participant-level max-T or cluster-corrected inference.
Decoding reports separate numerical evidence, methods, interpretation, limitations
and a results draft requiring scientific review. The code escape hatch is not
equivalent to validated structured coverage of every MNE API.
Export (1)
mne_save
Anything still not covered — BIDS, custom statistics, beamformers, autoreject — is reachable through
mne_run_code in the same live session. See TOOLS_REFERENCE.md for full
parameter details. Advanced dependencies are checked per feature and are not bundled.
Development
python -m compileall src/mne_mcp
pytest
mne-mcp status
mne-mcp setup --clients codex
License
MIT — see LICENSE
Documentation
Links