AlbumentationsX MCP
# AlbumentationsX MCP
Model Context Protocol server for [AlbumentationsX](https://github.com/albumentations-team/AlbumentationsX): inspect datasets, preview augmentations, refine them with visual feedback, and export reproducible pipelines.
<!-- mcp-name: io.github.dKosarevsky/albu-mcp -->
[](https://github.com/dKosarevsky/albu-mcp/actions/workflows/ci.yml)
[](https://pypi.org/project/albumentationsx-mcp/)
[](pyproject.toml)
[](https://registry.modelcontextprotocol.io/v0.1/servers?search=io.github.dKosarevsky/albu-mcp)
[](https://skills.sh/dKosarevsky/albu-mcp)

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`](https://github.com/dKosarevsky/albu-mcp/releases/latest/download/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:
```bash
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](docs/CONFIGURATION.md). Copyable host configurations are in [the install guide](docs/INSTALL.md).
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](docs/use-cases/CLASSIFICATION_ROBUSTNESS.md), or follow the
[First 10 Minutes guide](docs/FIRST_10_MINUTES.md). The `validate_preview_request` fallback, batch previews, and how to
compare preview runs are in [Usage](docs/USAGE.md). Use `too_noisy:high` or `exposure_too_weak:medium`, then optionally
share one redacted loop through [first-preview feedback](docs/FIRST_PREVIEW_FEEDBACK.md).
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](https://albumentations.ai/docs/integrations/mcp/)
- [Official MCP Registry entry](https://registry.modelcontextprotocol.io/v0.1/servers?search=io.github.dKosarevsky/albu-mcp)
- [skills.sh agent skill](https://skills.sh/dKosarevsky/albu-mcp)
- [Upstream documentation PR](https://github.com/albumentations-team/AlbumentationsX/pull/289)
## Documentation
- [Install and host configuration](docs/INSTALL.md)
- [Runtime settings and capability profiles](docs/CONFIGURATION.md)
- [First 10 minutes](docs/FIRST_10_MINUTES.md)
- [First-preview feedback](docs/FIRST_PREVIEW_FEEDBACK.md)
- [Usage](docs/USAGE.md) and [recipes](docs/RECIPES.md)
- [CPU Tensor Compose validation and export](docs/TORCH_CPU_COMPOSE.md)
- [MCP Apps review](docs/MCP_APPS_REVIEW.md) and [compatibility policy](docs/COMPATIBILITY.md)
- [Documentation index](docs/INDEX.md)
- [CHANGELOG.md](CHANGELOG.md)
- [server.json](server.json): public MCP Registry metadata.
## Development
```bash
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](LICENSE).
TDQS
Scored across 47 tools
Many tools have overlapping purposes, such as render_preview vs run_first_preview, recommend_recipe vs recommend_pipeline, and plan_augmentation_policy vs plan_augmentation_policy_candidates. While descriptions help, the boundaries between several tools are unclear, leading to potential misselection.
All tool names follow a consistent snake_case verb_noun pattern, which is a solid structure. However, the variety of verbs for similar actions (e.g., render vs run, recommend vs plan) and some compound names introduce minor inconsistencies in predictability.
With 47 tools, the server exceeds the 'too many' threshold (25+). The scope could be covered by a smaller, more focused set of tools, and the excessive count suggests over-engineering.
The tool surface covers the full workflow: pipeline recommendation, validation, export, preview rendering, feedback collection, tuning sessions, decisions, and environment diagnostics. No obvious gaps exist, though the redundancy in tools masks some minor missing operations like direct update/delete for certain entities.