The MCP server for AI agents

pardesline mcp exposes the datasets, the object model and the 73 algorithms to any Model Context Protocol client, on the user's machine, without an account. Twelve tools (list_datasets, describe_dataset, list_samples, load_sample, scan_dataset, confirm_dataset, export_dataset, list_methods, describe_method, run_method, describe_object, to_mesh), three resources and a start_here prompt. Questions the package will not guess (licence, units, array layout) come back as JSON for the agent to relay to the human.

pip install "pardesline[mcp]"
pardesline mcp                                            # stdio
pardesline mcp --transport streamable-http --port 8765    # http://127.0.0.1:8765/mcp

Client	Configuration
Claude Code	claude mcp add pardesline -- pardesline mcp (the repo ships a .mcp.json)
Claude Desktop, Cursor	"mcpServers": {"pardesline": {"command": "pardesline", "args": ["mcp"]}}
Gemini CLI	the same block in ~/.gemini/settings.json
Codex CLI	[mcp_servers.pardesline] command = "pardesline" args = ["mcp"] in ~/.codex/config.toml

If pardesline is not on your PATH, the command is python -m pardesline.mcp_server.

The skills/ folder holds twenty skills, grounded in the code and checked by the test suite (every module, method, parameter and dataset they mention must exist). Nine teach the package: using-pardesline (start here), catalog-and-run, object-model, datasets-and-manifests, scanning-private-folders, medical-volumes, cloud-client, mcp-server, contributing-algorithms. Eleven are expert guides with reference scripts for the domains the package serves, written for engineers who build the pipelines, and served to any MCP client through list_skills / get_skill:

Skill	What it carries	Runnable script
3d-representations	points vs meshes vs voxels vs splats: memory, architectures, the conversion matrix and its pitfalls	-
point-cloud-registration	RANSAC + FPFH + ICP, every parameter from one voxel size, metrics that lie, CPD	registration_eval.py (pose error on a known transform)
multimodal-rigid-registration	CT/MR with Mattes MI in SimpleITK: initialiser, optimizer scales, pyramid, validation by TRE	rigid_ct_mr.py (RIRE pair, metric trace, checkerboard)
atlas-label-transfer	labelled atlas -> unlabelled scan: registration, nearest-structure labels, per-label surfaces and volumes	atlas_label_transfer.py (99 % label agreement on a synthetic scan)
pointnet-segmentation	PointNet / PointNet++ step by step, data contract, training recipe	make_point_dataset.py (label maps -> labelled clouds), pointnet_seg.py (PyTorch)
dinov3-dense-features	Gram anchoring, frozen backbones, ViTDet pyramid + Faster R-CNN, medical transfer limits	dinov3_features.py, dinov3_faster_rcnn.py
jepa-pretraining	latent prediction instead of reconstruction, 3-D tokenisation, masking, collapse diagnostics	jepa3d_pretrain.py (3-D ViT, EMA target, block masks)
vae-anomaly-detection	normal-only training, residual maps, thresholds, honest limits	vae_anomaly.py (train / score / AUROC)
gaussian-splatting	the primitive, the five-step initialisation, PLY layout, training, medical uses	points_to_gaussians.py (any cloud -> 3DGS PLY)
medical-segmentation-models	nnU-Net, TotalSegmentator, transformers, preprocessing, inference, evaluation	prepare_nnunet_dataset.py (any dataset -> nnU-Net raw)
evaluation-metrics-3d	Dice / HD95 / NSD, chamfer / F-score, TRE, folding, AP, AUROC, PSNR, and how to report them	metrics3d.py

Install them as a plugin (it also registers the MCP server):

/plugin marketplace add pardesline/pardesline
/plugin install pardesline@pardesline