This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
open-med-mcp (OMM) is a Model Context Protocol server for medical image analysis. It gives AI coding agents (Claude Code, Codex CLI, Claude Desktop, Cursor, ...) tools to inspect a CT/MRI/X-ray, run segmentation and other models, view and measure the result, and write a report — all on your own machine.
Beyond segmentation it covers classification, detection, vision-language questions, radiomics, and computable clinical criteria (RECIST 1.1, Fleischner, Lung-RADS, LI-RADS, TI-RADS, Agatston). Three pillars:
pillar | what it means |
1. Containerized specialised models | Every model is an adapter with a manifest, a |
2. Preset / custom guidelines | Markdown protocols with YAML front matter that tell an agent how to do a task well (which model, which window, what to check, what to report). Presets ship with the package; drop your own into a folder to override or extend them. They are exposed as MCP prompts and resources. |
3. Modular, code-customizable viewer | For the agent, |
Images never leave the machine.
# 1. install (Python 3.10+)
pip install "open-med-mcp @ git+https://github.com/d0ng231/open-med-mcp"
# optional local model backends (or use containers, see below):
pip install "open-med-mcp[sam2,totalsegmentator] @ git+https://github.com/d0ng231/open-med-mcp" # pick extras: sam2,
# totalsegmentator, lungmask, hdbet, nnunet, monai, torchxrayvision - or [models] for all
# 2. check the environment and fetch weights you want to use
open-med-mcp doctor
open-med-mcp models download medsam2
# 3. register the server with your agent (run inside the folder that holds your images)
claude mcp add open-med-mcp -e OMM_WORKSPACE=$PWD -- open-med-mcp serve # Claude Code
codex mcp add open-med-mcp --env OMM_WORKSPACE=$PWD -- open-med-mcp serve # Codex CLI
open-med-mcp client-config claude-desktop # prints JSON for other clientsThen talk to your agent:
"Segment the liver in
ct.nii.gz, check the result in three planes, and give me the volume."
A typical run looks like this (tool calls made by the agent):
get_conventions() -> coordinate + prompt conventions
inspect_image("ct.nii.gz") -> 512x512x210, 0.8x0.8x1.5 mm, CT, RAS, preview
get_guideline("segmentation-3d-ct") -> protocol: TotalSegmentator first, MedSAM2 to refine
run_model("totalsegmentator", {"image": "ct.nii.gz"}, params={"roi_subset": ["liver"]})
render_view("ct.nii.gz", masks=[mask], layout="three-plane", window="soft-tissue") -> PNG (agent looks)
mask_to_prompts(mask) -> box prompt for a promptable model
segment("ct.nii.gz", model="medsam2", prompts=[{"type": "box", "coords": [...], "slice": 97}])
compare_masks(ts_mask, medsam2_mask, image="ct.nii.gz") -> Dice, HD95, volume difference
postprocess_mask(mask, ["largest_component", "fill_holes"])
mask_stats(mask, image="ct.nii.gz") -> 1432 mL, mean 58 HU
write_report("Liver volumetry", sections=[...])Related MCP server: LLM Pre-Read MCP
group | tools |
inspect |
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models |
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clinical criteria |
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masks |
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processing |
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viewer |
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guidance |
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Full reference with every parameter: docs/tools.md. Coordinate conventions: docs/coordinates.md.
model | what it does | modalities | prompts | how it runs |
| MedSAM2: promptable 2D + 3D segmentation (slice propagation), medical fine-tune of SAM 2.1 | CT, MR, PET, US, endoscopy | box, points | local |
| SAM 2.1 tiny / small / base+ / large | any | box, points | local |
| VoxTell (CVPR 2026): free-text prompts ("liver", "left kidney", "liver tumor") -> 3D masks | CT, MR, PET | text | local |
| 117 CT structures, MR variant, vessels, body regions ... | CT, MR | - | local |
| lungs (R231) and lobes (LTRCLobes) + LAA% emphysema index | CT | - | local |
| HD-BET 2.0 brain extraction (mask + stripped image) | MR | - | local |
| FreeSurfer SynthStrip skull stripping, official image wrapped | MR, CT, PET | - | Docker, Apptainer (host |
| any nnU-Net v2 model (results folder, exported zip, dataset name) | any | - | local |
| MONAI Model Zoo bundles: segmentation (spleen, pancreas, whole body, BraTS, prostate ...) and detection (lung nodules, RetinaNet) | CT, MR | - | local |
| chest X-ray: 18-finding classification, 14-structure anatomy segmentation (-> cardiothoracic ratio), biological age | XR | - | local |
| vision-language: MedGemma 4B by default (any HF image-text-to-text model, e.g. Qwen2.5-VL) - describe, answer, draft | any | text | local |
| pyradiomics: IBSI feature extraction (shape, first order, GLCM, GLRLM, GLSZM, GLDM, NGTDM, filters) per label | CT, MR, PET | - | local (Python 3.9 venv), Docker, Apptainer |
| threshold / Otsu / multi-range / seeded region growing | any | seeds | local (no extras), Docker, Apptainer |
open-med-mcp models list # what is available
open-med-mcp models check hdbet # which backend can run it here
open-med-mcp models pull medsam2 # docker pull ghcr.io/d0ng231/open-med-mcp-sam2:0.1
open-med-mcp models pull synthstrip --engine apptainer # HPC: official image -> .sif
open-med-mcp models build lungmask --engine apptainer # or build from the Dockerfile (converted to a .def)
open-med-mcp run lungmask --image chest_ct.nii.gz --task lobes
open-med-mcp run medsam2 --image ct.nii.gz --prompts '[{"type":"box","coords":[60,80,20,140,170,20]}]'
open-med-mcp run voxtell --image ct.nii.gz --prompts '[{"type":"text","text":"liver"},{"type":"text","text":"spleen"}]'Adding a model = copy src/open_med_mcp/zoo/_template/, edit the manifest, implement run.py
against the job contract, add a Dockerfile. Wrapping an existing image (as
synthstrip does) needs only a manifest with a command template. User models can live outside the
package (OMM_MODEL_DIRS). See docs/models.md.
Workflow presets: getting-started, segmentation-3d-ct, segmentation-3d-mri, segmentation-2d-prompted,
brain-mri-preprocessing, chest-xray-triage, chest-xray-anatomy-and-ctr, lung-ct-analysis,
registration-followup, batch-processing, multi-organ-ct-report, compare-two-segmentations, qc-checklist.
Clinical criteria presets (each cites its source and states its scope): recist-1-1, fleischner-2017,
lung-rads-2022, li-rads-2018, acr-ti-rads-2017, coronary-calcium-agatston,
organ-volume-reference-ranges.
Add your own: put *.md files with YAML front matter into omm_guidelines/ in the workspace (or any
directory in OMM_GUIDELINE_DIRS). A file with the same name overrides the preset. Guidelines
are also exposed as MCP prompts (/mcp__open-med-mcp__segmentation-3d-ct in Claude Code) and as
guideline://<name> resources. See docs/guidelines.md.
render_view(...) - PNG figures for the agent, with native-voxel tick labels.
export_viewer(...) - one self-contained HTML file (no server, no CDN): cine/scroll through slices,
toggle the overlay, invert, zoom/pan, and click or drag for a point/box prompt in native
coordinates (with a millimetre size) that feeds back into segment.
open-med-mcp serve-viewer ct.nii.gz -m mask.nii.gz - 3D view (NiiVue) on localhost.
write_report(...) - Markdown + HTML with embedded figures.
Custom renderers: implement render(image, masks, spec) and register with @register_renderer.
See docs/viewer.md.
automatic anatomy: | free-text prompts: |
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skull stripping: | chest X-ray: |
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2D promptable: | agent QC view: |
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chest X-ray anatomy + | classify + measure + criteria |
| RECIST |
All figures are real tool outputs on public sample data (abdominal CT, MNI152 template, NIH chest
X-ray; see examples/get_sample_data.sh).
Everything is extensible from the workspace, without forking:
open-med-mcp new plugin lesion-count # omm_plugins/lesion_count.py: register(server) + your @server.tool()s
open-med-mcp new model my-unet # omm_models/my-unet/: manifest + run.py + Dockerfile (job contract)
open-med-mcp new model synthseg --wrapped-image freesurfer/synthseg # manifest only, drives the official image
open-med-mcp new guideline my-protocol # omm_guidelines/my-protocol.md
open-med-mcp plugins list # what loads, and why something did notPlug-in tools use open_med_mcp.plugin_api (path resolution, cached image loading, previews,
result packaging) and appear next to the built-in tools; packaged plug-ins register through the
open_med_mcp.plugins entry point. See docs/plugins.md and examples/plugins/.
stdio hygiene - the server never writes to stdout; logs go to stderr and $OMM_HOME/logs/server.log.
Long runs - model tools stream MCP progress notifications; pass wait=false to get a job id
immediately and poll get_job (results, previews and logs are also on disk under omm_outputs/).
Concurrency - tools run in worker threads; rendering, caches and provenance are lock-protected.
Payload limits - inline previews are capped (OMM_MAX_IMAGE_BYTES, default 1.5 MB) and can be
switched off for text-only clients (OMM_RETURN_IMAGES=0); every result also names the saved file.
Transports - open-med-mcp serve (stdio), --transport streamable-http / sse for remote
agents; over HTTP file access is confined to the workspace by default.
Errors - every tool returns a readable is_error result instead of crashing the session.
Schemas - every parameter is typed and described; the test-suite validates all tool schemas.
variable | default | meaning |
| current directory | root for relative paths; outputs go to |
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| weights and Apptainer images |
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| current interpreter | interpreter of a dedicated venv for a local adapter (e.g. |
| - | extra adapters / guidelines ( |
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| registry prefix for container images |
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| confine file access to the workspace |
| - | extra plug-in directories ( |
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| inline preview images in results |
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| cap for one inline image |
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| logging |
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| in-memory image cache budget |
No Docker on the cluster? open-med-mcp models pull <model> --engine apptainer fetches the GHCR
image as a .sif, or models build --engine apptainer converts the Dockerfile to an Apptainer
definition and builds it. Set OMM_RUNNER=apptainer; GPU passthrough (--nv) is automatic when a
GPU is visible. Alternatively point OMM_ZOO_SAM2_PYTHON at a venv with PyTorch and run models on
a GPU node with the local backend. See docs/hpc.md.
Architecture - components, data flow, design decisions
Quick start and client setup - Claude Code, Codex, Claude Desktop, Cursor, HTTP transport
Plug-ins - tools, models, guidelines, renderers
git clone https://github.com/d0ng231/open-med-mcp && cd open-med-mcp
uv venv && uv pip install -e ".[dev]"
.venv/bin/ruff check src tests && .venv/bin/python -m pytest -q # CPU-only tests, ~1 minThe test-suite covers the whole stack (I/O, coordinates, processing, viewer, job contract, MCP server, CLI) with synthetic data; the real models are verified on GPU/CPU before a release.
SAM 2 (Meta, Apache-2.0) and MedSAM2 (Ma et al., Apache-2.0)
TotalSegmentator (Wasserthal et al., Apache-2.0), lungmask (Hofmanninger et al., Apache-2.0), HD-BET and nnU-Net (Isensee et al., Apache-2.0), SynthStrip (Hoopes et al., FreeSurfer license), MONAI (Apache-2.0), TorchXRayVision (Cohen et al., Apache-2.0)
SimpleITK, NiiVue, the MCP Python SDK
Model weights are downloaded from the upstream projects and remain subject to their licenses.
open-med-mcp is research software. It is not a medical device and must not be used for clinical decision making. Outputs are model predictions that need expert review.
Apache-2.0. See LICENSE. If you use it in research, please cite via CITATION.cff.
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PDF and image tools for AI agents: compress, merge, split, OCR, sign. Runs locally, never uploads.
Telehealth support for Claude, Codex and Cursor: investigate cases and connect your tools.
Physician-reviewed medical opinions and prescriptions for AI agents.
Medical RAG: semantic search for clinical guidelines, drug interactions, diagnoses & EHR data.