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 --help output.

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") → ✅ COMPLETED

The server exposes 41 tools covering the complete single-particle analysis pipeline, the RELION 5 tomography (subtomogram-averaging) pipeline, results-interpretation tools that let an agent make its own decisions, and STAR/image utilities.

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.

7. RELION GUI Integration

Every launched job is registered in the project's default_pipeline.star — with the correct RELION 5 process/node type labels, live status, and input/output edges — so a project driven by this server opens cleanly in the RELION GUI with a connected job graph. Best-effort and atomic; disable with RELION_WRITE_PIPELINE=0.

8. Tomography (Subtomogram Averaging)

The full RELION 5 STA numerical pipeline — import tilt series, reconstruct tomograms, extract pseudo-subtomograms, reconstruct particle, tomo CTF-refine and frame-align — is exposed as dedicated relion_tomo_* tools that build on the tomography optimisation-set data model and register with the correct tomo pipeline labels.

9. Results-Driven Decisions

Instead of forcing the agent to hand-parse STAR files, relion_job_results returns the metrics that actually drive the next decision (resolution, ranked class distributions), relion_select_classes acts on them numerically, and relion_render_classes lets a vision-capable agent see the 2D classes. Failed jobs come back with an actionable hint rather than a raw traceback.

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 (17 tools — all with preview/confirm)

Read-Only Tools (7 tools)

Control Tool (1 tool)

Tomography / STA Tools (8 tools — all with preview/confirm)

Based on the RELION 5 subtomogram-averaging tutorial. 3D refinement/classification of the pseudo-subtomograms reuses the existing relion_class3d / relion_refine3d tools.

Tilt-series alignment (AreTomo2/IMOD), denoising (cryoCARE) and template-match picking are external-tool steps in RELION 5 and are not wrapped as dedicated tools — run them with their native tools or via relion_run_command. The RELION-native numerical path (import → reconstruct particle, plus tomo CTF-refine and frame-align) is fully covered.

Tomo flags are taken from the RELION 5 reference docs (generated from each program's --help) but were not validated against a live binary; verify with relion_help(program="relion_tomo_…") on your build if a run fails.

Results & Decision Tools (3 tools)

Turn the agent from a blind operator into a decision-maker: read metrics and select classes without hand-parsing STAR files.

Utility Tools (5 tools)

Also: relion_read_star now has a summary=true mode returning per-column count/min/mean/max instead of dumping rows, and job errors come with an actionable Hint (out-of-memory, GPU, missing path, MPI, STAR-label, binary-not-found).

Tutorial Defaults (EMPIAR-10204)

All defaults match the RELION 5 beta-galactosidase tutorial:

Prerequisites

• RELION 5.x compiled and in PATH

• Python ≥ 3.10

• FastMCP and dependencies (see requirements.txt):

  pip install -r requirements.txt

  • fastmcp >= 3.0 (bundles the mcp SDK and uvicorn for HTTP mode)

  • pydantic >= 2.0.0

Installation

git clone https://github.com/kdursunnizam-art/relion-mcp-server.git
cd relion-mcp-server
pip install -r requirements.txt

Optionally, use a virtual environment (recommended for HTTP mode):

cd relion-mcp-server
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Usage

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 user

Verify:

claude mcp list

Remove / reconfigure:

claude mcp remove relion

Note: --scope user makes the server available in all your projects.

HTTP (remote) — EXPERIMENTAL

1. 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 0.0.0.0

Keep this terminal open.

2. Register the running server with Claude Code:

claude mcp add --transport http relion http://YOUR.IP.ADDRESS:8000/mcp --scope user

3. Verify:

claude mcp list

It 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.json

• macOS: ~/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 list

HTTP (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 0.0.0.0

Register:

openclaw mcp add --transport http --scope user relion http://YOUR.IP.ADDRESS:8000/mcp

Or 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

Security

• Only relion_* executables can be run (validated)

• No shell injection: synchronous calls use subprocess.run with an argument list (no shell=True). Background jobs run through a generated run.sh, but every argument and path is escaped with shlex.quote, so filenames containing spaces or shell metacharacters are passed literally and can never be executed.

• File paths are resolved relative to the project directory. Set RELION_CONFINE_PATHS=1 to additionally reject any path that resolves outside the project directory (off by default, since raw movies/gain refs often live on a separate scratch disk).

• HTTP mode is unauthenticated. It binds to 127.0.0.1 by default. For remote access set --host 0.0.0.0 — the server then prints a warning, because this exposes tools that can launch RELION binaries (including relion_run_command). Only do this on a trusted, firewalled network, ideally behind an authenticating reverse proxy.

• Preview/confirm prevents accidental job launches; relion_abort can stop a running job.

Framework: FastMCP

Built on standalone FastMCP 3.x (fastmcp>=3.0) — the actively-developed, community-standard framework, which bundles the official mcp SDK underneath.

• Import: from fastmcp import FastMCP.

• Server is launched with mcp.run(transport="stdio") or mcp.run(transport="streamable-http", host=..., port=...).

• All tool functions are async and take a single Pydantic model parameter; FastMCP builds the JSON schema from it.

• Passes python3 -m py_compile cleanly and the tests/ suite (pytest).

Migrated from the FastMCP 1.x bundled inside the official mcp SDK (mcp.server.fastmcp). That module was removed when the official SDK reached 2.0 (renamed to MCPServer), so this server now depends on the standalone package instead.

Changelog

v3.4 (current)

• Agentic decision loop (P1): relion_job_results (auto-detecting metrics extractor: resolution, per-class distribution/estimated-resolution ranking, CTF/motion stats), relion_select_classes (deterministic numeric class selection → particles.star), and relion_render_classes (2D class-average montage returned as an inline image).

• Coverage (P2): relion_star_handler, relion_image_handler, relion_subtract (signal subtraction), relion_symmetry_expand.

• Polish (P3): actionable error Hints on failed jobs (out-of-memory / GPU / missing path / MPI / STAR-label / binary), relion_read_star summary mode (per-column stats), and relion_tomo_latest_optimisation_set.

• 41 tools total. Optional deps numpy + pillow (only for relion_render_classes).

v3.3

• Tomography (STA) pipeline added — 8 new tools (relion_tomo_import, relion_tomo_import_coords, relion_tomo_reconstruct_tomogram, relion_tomo_subtomo, relion_tomo_reconstruct_particle, relion_tomo_ctf_refine, relion_tomo_align, relion_tomo_suggest_next_step), following the RELION 5 subtomogram-averaging tutorial. Flags taken from the RELION 5 reference docs.

• Pipeline node-rules re-keyed by process label (not directory), so the directories shared between SPA and tomo (Import, Extract, CtfRefine, Polish, Reconstruct) register with the correct type/node labels for each. Full default_pipeline.star integration with authoritative tomo labels (relion.importtomo, relion.pseudosubtomo, relion.ctfrefinetomo, relion.framealigntomo, …). 33 tools total.

v3.2

• Migrated to standalone FastMCP 3.x (from fastmcp import FastMCP, fastmcp>=3.0) — the actively-developed, community-standard framework — replacing the FastMCP 1.x bundled in the official mcp SDK (removed in SDK 2.0). Tool schemas, annotations and behaviour are unchanged; host/port are now passed to mcp.run(...).

v3.1

• Security fix — background jobs: run.sh is now generated with shlex.join/shlex.quote, closing a shell-injection hole and fixing jobs breaking on paths/filenames containing spaces.

• Bug fix — STAR parser: relion_read_star now parses all data blocks and selects the correct one (auto-picks the main table, or an explicit data_particles/particles). Previously it merged data_optics + data_particles and returned garbage on standard particle files.

• RELION GUI integration: every job is now registered in the project's default_pipeline.star (processes, nodes, input/output edges) so jobs created by this server appear — correctly typed, connected, and status-tracked — when the project is opened in the RELION GUI. Label strings are taken verbatim from RELION 5 src/pipeline_jobs.h. Writes are atomic (.bak kept), merge-safe with existing pipelines, and best-effort (never block a launch). Toggle with RELION_WRITE_PIPELINE=0.

• Global job numbering: job directories now share one counter across all job types (job001, job002, …), matching RELION's own scheme.

• New tools: relion_select (auto class selection via relion_class_ranker) and relion_abort (stop a running job).

• Hardening: optional RELION_CONFINE_PATHS, PID-file based liveness (no more pgrep), unauthenticated-HTTP warning on non-local bind.

• Cleanup: removed a dead validator, standardised runtime messages to English, pinned mcp>=1.26,<2.0, added a pytest suite. 25 tools total.

v3

• 68 missing params added, 11 defaults fixed, 3 MPI validations

• GPU support (--gpu) on Class2D, InitialModel, Class3D, Refine3D

• Blush 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_padding

• 2 new tools: relion_local_resolution, relion_modelangelo

• CTF 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 time

• relion_job_status enhanced: PID liveness detection, stderr tail on failure, RUNNING vs IDLE

• relion_help: run relion_* --help and parse all flags live, with keyword filtering

• Wrapper script (run.sh) in each job_dir auto-creates SUCCESS/FAILURE markers

• 21 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_help

• Parameters 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.

• Model Context Protocol

• RELION Documentation

• Steinberger, P. (2025). OpenClaw: An open-source autonomous AI agent (Version 2026.x.x) [Computer software]. GitHub. https://github.com/openclaw/openclaw