RELION MCP Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@RELION MCP ServerRun motion correction on imported movies"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
RELION MCP Server v3
An MCP (Model Context Protocol) server that lets AI agents drive RELION 5.x — the gold-standard software for cryo-EM structure determination.
Tested and verified against RELION 5.0.1 on Ubuntu 24.04 (WSL2). All CLI flags validated against actual
--helpoutput.
What It Does
An AI agent (Claude Code, OpenClaw, NemoClaw, etc.) can process cryo-EM data through natural language:
You: "Import the movies from Movies/*.tiff, 200 kV, pixel size 0.885 Å, then run motion correction"
Agent: → relion_import(..., confirm=False) → shows parameter preview
You: "Looks good, launch it"
Agent: → relion_import(..., confirm=True) → job runs (instant)
→ relion_motioncorr(..., confirm=False) → preview
You: "Ok go"
Agent: → relion_motioncorr(..., confirm=True) → 🚀 Launched (PID 12345)
→ relion_job_status("MotionCorr/job001") → 🔄 RUNNING
→ relion_job_status("MotionCorr/job001") → ✅ COMPLETEDThe server exposes 23 tools covering the complete single-particle analysis pipeline.
Related MCP server: Fiji MCP Server
Key Features
1. Preview Before Launch
Every pipeline tool: confirm=False shows all parameters (✏️ user / 📋 tutorial default / ❌ missing / ⬜ optional), confirm=True launches the job.
2. Non-Blocking Background Execution
All long-running jobs launch via detached Popen and return immediately with PID. Monitor with relion_job_status and relion_job_logs.
3. GPU Support
Class2D, InitialModel, Class3D, and Refine3D all expose --gpu for GPU acceleration.
4. Blush Regularisation
RELION 5's neural-network prior is available on Class3D and Refine3D via use_blush=True.
5. VDAM Algorithm
Class2D and InitialModel support the VDAM gradient algorithm via use_vdam=True, with MPI=1 validation.
6. Live Flag Discovery
relion_help runs relion_* --help in real time with keyword filtering.
Architecture
AI Agent (Claude Code / OpenClaw / NemoClaw)
│
│ stdio or HTTP
▼
RELION MCP Server v3 (Python)
│
│ Popen (detached) subprocess.run (short jobs)
▼ ▼
RELION 5.x binaries relion_import, relion_help
(background, non-blocking) (synchronous, fast)Tools
Pipeline Tools (16 tools — all with preview/confirm)
Tool | Binary |
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Read-Only Tools (7 tools)
Tool | Description |
| Project overview |
| Parse STAR files |
| Job status + PID detection + stderr tail |
| Read stdout/stderr from background jobs |
| Recommend next step (14-step pipeline) |
| Run any |
| Parse |
Tutorial Defaults (EMPIAR-10204)
All defaults match the RELION 5 beta-galactosidase tutorial:
Step | Key defaults |
Import | 200 kV, 0.885 Å, Cs 1.4, Q0 0.1 |
MotionCorr | dose 1.277, patches 5×5, bfactor 150, float16, save_ps |
CTF | Box 512, 30-5 Å, dF 5000-50000, dAst 100, use_given_ps=True |
AutoPick | LoG, 150-180 Å, upper_threshold=5, maxres=20 |
Extract | box 256 → 64, invert, bg_radius 200 |
Class2D | K=50, T=2, mask 200, CTF, center |
InitialModel | VDAM 100 mini-batches, T=4, C1 + apply_sym_later |
Class3D | K=4, T=4, C1, ini_high 50, healpix 2 |
Refine3D | D2, ini_high 50, MPI=3 (odd≥3), pool 30 |
Mask | lowpass 15, threshold 0.01, extend 3, soft_edge 8 |
PostProcess | auto B-factor, autob_lowres 10 |
CTF Refine | All flags off by default (multi-pass workflow) |
Polishing | Train/Polish modes, sigma vel/div/acc, float16 |
Prerequisites
RELION 5.x compiled and in
PATHPython ≥ 3.10
MCP Python SDK and dependencies (see
requirements.txt):pip install -r requirements.txtmcp >= 1.0.0
pydantic >= 2.0.0
uvicorn >= 0.30.0 (for HTTP mode)
Installation
git clone https://github.com/kdursunnizam-art/relion-mcp-server.git
cd relion-mcp-server
pip install -r requirements.txtOptionally, use a virtual environment (recommended for HTTP mode):
cd relion-mcp-server
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txtUsage
With Claude Code (recommended for local use)
stdio (local)
From your terminal:
claude mcp add-json relion '{"command":"python3","args":["/path/to/relion-mcp-server/relion_mcp.py"],"env":{"RELION_PROJECT_DIR":"/path/to/data/relion_tutorial"}}' --scope userVerify:
claude mcp listRemove / reconfigure:
claude mcp remove relionNote: --scope user makes the server available in all your projects.
HTTP (remote) — EXPERIMENTAL
Start the server manually in a terminal:
cd /path/to/relion-mcp-server
source venv/bin/activate
export RELION_PROJECT_DIR=/path/to/data/relion_tutorial
python relion_mcp.py --transport http --port 8000 --host 0.0.0.0Keep this terminal open.
Register the running server with Claude Code:
claude mcp add --transport http relion http://YOUR.IP.ADDRESS:8000/mcp --scope userVerify:
claude mcp listIt should show relion with the HTTP transport and URL http://YOUR.IP.ADDRESS:8000/mcp.
Then in Claude Code:
> Use relion_project_info to show the project status
> Import movies from Movies/*.tiff with pixel size 0.885, 200 kV, Cs 1.4
> Run motion correction with dose 1.277 e-/Ų/frame and gain ref Movies/gain.mrc
> Show me the Class2D parameters before running (agent calls with confirm=False)
> Change threads to 8 and launch (agent calls with confirm=True)With Claude Desktop
Claude Desktop only supports stdio servers via manual config. Edit claude_desktop_config.json:
Windows:
%APPDATA%\Claude\claude_desktop_config.jsonmacOS:
~/Library/Application Support/Claude/claude_desktop_config.json
{
"mcpServers": {
"relion": {
"command": "python3",
"args": ["/path/to/relion-mcp-server/relion_mcp.py"],
"env": {
"RELION_PROJECT_DIR": "/path/to/data/projet_relion",
"RELION_THREADS": "4",
"RELION_MPI": "1"
}
}
}
}On Windows with WSL2, set "command": "wsl" and prepend python3 to args:
{
"mcpServers": {
"relion": {
"command": "wsl",
"args": ["python3", "/home/you/relion-mcp-server/relion_mcp.py"],
"env": { "RELION_PROJECT_DIR": "/home/you/relion_tutorial" }
}
}
}Restart Claude Desktop after editing the config.
With OpenClaw / NemoClaw
stdio (local)
openclaw mcp add --transport stdio --scope user relion --cmd python3 --args "/path/to/relion-mcp-server/relion_mcp.py" --env RELION_PROJECT_DIR="/path/to/data/relion_tutorial"Verify:
openclaw mcp listHTTP (remote)
Start the server:
cd /path/to/relion-mcp-server
source venv/bin/activate
export RELION_PROJECT_DIR=/data/my_project
python relion_mcp.py --transport http --port 8000 --host 0.0.0.0Register:
openclaw mcp add --transport http --scope user relion http://YOUR.IP.ADDRESS:8000/mcpOr configure openclaw.json manually (both stdio and HTTP):
{
"skills": {
"install": { "nodeManager": "npm" },
"entries": {
"mcp-integration": {
"enabled": true,
"config": {
"servers": [
{
"name": "relion-stdio",
"transport": "stdio",
"command": "python3",
"args": ["/path/to/relion-mcp-server/relion_mcp.py"],
"env": { "RELION_PROJECT_DIR": "/path/to/data/projet_relion" }
},
{
"name": "relion-http",
"transport": "streamable-http",
"url": "http://YOUR.IP.ADDRESS:8000/mcp"
}
],
"toolPrefix": true
}
}
}
}
}Configuration
Environment Variable | Description | Default |
| RELION project directory | Current directory |
| Path prefix for RELION binaries | (uses PATH) |
| Default thread count | 4 |
| Default MPI processes | 1 |
CLI Flag | Description | Default |
|
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| HTTP port | 8000 |
| HTTP host (use | 127.0.0.1 |
| Override | (env or cwd) |
Security
Only
relion_*executables can be run (validated)No shell injection: subprocess calls do not use
shell=TrueFile paths resolved relative to the project directory
In HTTP mode, the server binds to
127.0.0.1by default. For remote access, set--host 0.0.0.0(or your specific IP). Be aware this exposes the server on your network — use only on trusted networks.Preview/confirm prevents accidental job launches
MCP SDK Compatibility
Designed for MCP SDK 1.26+ with these constraints:
No
lifespan(causes crash with MCP SDK 1.26)No
ctx.report_progress(causes crash with MCP SDK 1.26)All tool functions are
asyncwithout aContextparameterPasses
python3 -m py_compilecleanly
Changelog
v3 (current)
68 missing params added, 11 defaults fixed, 3 MPI validations
GPU support (
--gpu) on Class2D, InitialModel, Class3D, Refine3DBlush on Class3D, Refine3D
VDAM on Class2D, InitialModel (with MPI=1 validation)
Polishing fully rewritten: train/polish modes, sigma params, opt_params
Compute params factored:
--pool,--preread_images,--scratch_dir,--skip_padding2 new tools:
relion_local_resolution,relion_modelangeloCTF Refine fixed: +beamtilt, +fit_phase, +minres, defaults all False
Mask Create defaults fixed to match tutorial
HTTP host/port now correctly applied from CLI flags
23 tools total
v2.1
Background execution: long-running jobs launch via
Popen(start_new_session=True)and return immediately with PID. No more agent blocking.relion_job_logs: read stdout/stderr from background jobs in real timerelion_job_statusenhanced: PID liveness detection, stderr tail on failure, RUNNING vs IDLErelion_help: runrelion_* --helpand parse all flags live, with keyword filteringWrapper script (
run.sh) in each job_dir auto-creates SUCCESS/FAILURE markers21 tools total
v2.0
Preview/confirm system on all pipeline tools
5 new tools:
relion_initial_model,relion_mask_create,relion_ctf_refine,relion_bayesian_polishing,relion_helpParameters added: bfactor, gain_rot/flip, float16, save_ps, d_ast, phase shift, invert_contrast, white/black dust, --ctf flag, center_classes, healpix_order, skip_gridding, ref_correct_greyscale, MPI validation, autob_lowres/highres, mtf_angpix, skip_fsc_weighting
Tutorial defaults from EMPIAR-10204 baked in
20 tools total
v1.0
Initial release with 15 tools
Verified against RELION 5.0.1
Tested With
RELION 5.0.1 (commit cad71bf)
Ubuntu 24.04 LTS (WSL2)
Python 3.12, MCP SDK 1.26.0
Claude Code 2.1.89
OpenClaw 2026.4.2 (commit d74a122)
Tutorial dataset: beta-galactosidase (EMPIAR-10204)
License
MIT — RELION itself is GPLv2. This server interacts with RELION solely through its CLI.
References
Scheres, S.H.W. (2012). RELION: Implementation of a Bayesian approach to cryo-EM structure determination. J. Struct. Biol. 180(3), 519–530.
Kimanius, D. et al. (2021). New tools for automated cryo-EM single-particle analysis in RELION-4.0. Biochem. J. 478(24), 4169–4185.
Steinberger, P. (2025). OpenClaw: An open-source autonomous AI agent (Version 2026.x.x) [Computer software]. GitHub. https://github.com/openclaw/openclaw
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