Albu

Model Context Protocol server for AlbumentationsX: inspect datasets, preview augmentations, refine them with visual feedback, and export reproducible pipelines.

CI PyPI Python MCP Registry skills.sh

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.

Install

Claude Desktop

Download the latest albumentationsx-mcp.mcpb, install it from Settings -> Extensions -> Advanced settings, and select separate image and artifact directories.

Other MCP Hosts

Run the published server with bounded local access:

uvx --from albumentationsx-mcp albumentationsx-mcp \
  --allowed-root /absolute/path/to/images \
  --artifact-root /absolute/path/to/albu-artifacts

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:

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".

Follow the First 10 Minutes guide. Detailed guided and advanced workflows, including the explicit validate_preview_request fallback, batch previews, and how to compare preview runs, are in Usage. Give concrete feedback such as too_noisy:high or exposure_too_weak:medium before accepting a result.

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.

Integrations

• Official Albumentations MCP guide
• Official MCP Registry entry
• skills.sh agent skill
• Upstream documentation PR

Documentation

• Install and host configuration
• Runtime settings and capability profiles
• First 10 minutes
• Usage and recipes
• CPU Tensor Compose validation and export
• MCP Apps review and compatibility policy
• Documentation index
• CHANGELOG.md
• server.json: public MCP Registry metadata.

Development

uv sync --all-extras --dev
uv run pytest
uv run ruff check .
uv run ruff format --check .
uv run ty check

Licensed under AGPL-3.0-or-later.