AlbumentationsX MCP for batch previews, compare preview runs, segmentation masks, and exports.
io.github.dKosarevsky/albu-mcp — AlbumentationsX MCP Server
Model Context Protocol (MCP) server for AlbumentationsX that supports dataset inspection, batch previews, and visual refinement. It also enables comparing preview runs, working with segmentation masks, and exporting reproducible pipelines.
🛠️ Key Features
Batch previews and preview-run comparisons
Segmentation mask support
Export of reproducible augmentation pipelines
🚀 Use Cases
Inspect AlbumentationsX datasets
Preview and iteratively refine image augmentations with visual feedback
Validate changes by comparing preview runs
⚡ Developer Benefits
Reproducible pipeline export for reuse
Alignment with Model Context Protocol (MCP) and tool-based workflows
Python-based implementation for computer-vision augmentation
⚠️ Limitations
No additional capabilities beyond dataset inspection, preview/compare workflows, segmentation masks, and pipeline export are described in the provided data.
Model Context Protocol server for AlbumentationsX: inspect datasets, preview augmentations, refine them with visual feedback, and export reproducible pipelines.
Ask an MCP host for several robustness variants, reject an excessive result such as too_noisy:high, compare the adjusted batch previews, and export the accepted pipeline.
run_first_preview requires the default full or dataset capability profile. The smaller review profile uses the
explicit validate/render fallback in the usage guide, or you can restart with dataset or full; see
configuration. Copyable host configurations are in the install guide.
The repository also contains a native Codex plugin bundle. npx skills add dKosarevsky/albu-mcp installs agent guidance, not the MCP server.
First Preview
After connecting the server, ask your host:
text
Run the host smoke check. If preview_ready is true, call run_first_preview for /absolute/path/to/images with low
intensity and at most 8 images. Show me the contact sheet. When I mention a specific result, call
trace_preview_variant before adjusting it.
run_host_smoke_check returns preview_ready and a preview_request_template. If resource reads are unavailable, call
get_workflow_example with example_id="client-smoke".
Try the classification robustness use case, or follow the
First 10 Minutes guide. The validate_preview_request fallback, batch previews, and how to
compare preview runs are in Usage. Use too_noisy:high or exposure_too_weak:medium, then optionally
share one redacted loop through first-preview feedback.
If setup fails, read albumentationsx://diagnostics/guide and call diagnose_environment for bounded remediation actions.
Capabilities
Transform discovery, schemas, recipes, and pipeline validation.
Classification, detection, segmentation, OCR, bbox, mask, keypoint, and dataset-quality workflows.
Deterministic previews, contact sheets, annotation overlays, comparison, ranking, and reports.
Interactive MCP Apps review with a text-only fallback for other hosts.
Structured feedback, tuning sessions, and Python, JSON, or YAML export.
Runtime-aware CPU torch.Tensor pipeline validation and guarded Python handoff.
MCP 2026-07-28 plus legacy negotiation; stable agent workflow resources, diagnostics, and contract snapshots.
The server does not execute arbitrary Python, fetch remote images, overwrite datasets, or train models. Reads are restricted by --allowed-root; generated files stay under --artifact-root.
