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Model Context Protocol server for AlbumentationsX augmentation discovery, validation, and previews.

Project description

AlbumentationsX MCP

Model Context Protocol server for AlbumentationsX: transform discovery, pipeline validation, deterministic previews, feedback loops, and reproducible exports for computer vision augmentation work.

CI PyPI Python MCP Registry

Purpose

AlbumentationsX MCP is a thin, typed MCP layer around existing AlbumentationsX primitives. It helps MCP hosts:

  • discover transforms and schemas from albu-spec;
  • recommend and validate augmentation pipelines;
  • render local batch previews and compare preview runs;
  • record concrete feedback such as too_noisy:high;
  • export accepted pipelines and review reports.

The server does not execute arbitrary Python, fetch remote images, overwrite datasets, or train models. Local preview access is bounded by --allowed-root, and generated artifacts are written under --artifact-root.

Quick Start

Run the published server:

uvx --from albumentationsx-mcp albumentationsx-mcp

For local development:

uv sync --all-extras --dev
uv run albumentationsx-mcp

For preview work, scope filesystem access explicitly:

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

Copyable host snippets are in examples. Full host setup is in docs/INSTALL.md.

Host Workflow

After connecting an MCP host:

  1. Read albumentationsx://examples/client-smoke.
  2. Call run_host_smoke_check.
  3. Continue only when preview_ready is true.
  4. For a real folder, call plan_dataset_onboarding to sample local images and detect common dataset layouts safely.
  5. Replace or reuse the paths in preview_request_template.request.
  6. Call validate_preview_request before rendering user-provided paths.
  7. Call render_preview_batch on a small local image set.
  8. Inspect the contact sheet, then use adjust_pipeline, compare_preview_runs, and export_pipeline.

If preview setup fails, read albumentationsx://diagnostics/guide and call diagnose_environment. Troubleshooting details and remediation_actions are documented in docs/USAGE.md and docs/INSTALL.md.

Capabilities

  • Transform search and schema inspection.
  • Recipe and pipeline recommendation for classification, detection, segmentation, OCR, and balanced workflows.
  • Pipeline validation and explanation before rendering.
  • Preview request validation for missing files, outside-root paths, masks, and annotation counts.
  • Read-only dataset onboarding that detects common layouts and builds annotation-aware first-preview templates.
  • Deterministic single-image and batch previews with contact sheets.
  • Preview comparison with quality_summary and suggested feedback tags.
  • Concrete preview feedback, interactive tuning sessions, ranking, dataset scoring, and visual reports.
  • Agent workflow resources, prompts, smoke checks, diagnostics, and release-safe contract snapshots.

The public MCP surface is kept stable through reviewed contract snapshots. Compatibility rules are in docs/COMPATIBILITY.md.

Documentation

Operational scripts live in scripts and are covered by the verification commands below.

Verification

uv run pytest
uv run ruff check .
uv run ruff format --check .
uv run ty check
uv run python scripts/validate_host_manual_runs.py
uv run python scripts/check_host_acceptance_report.py
uv run python scripts/check_contract_snapshots.py
uv run python scripts/check_demo_assets.py --output-dir docs/assets/demo --check
uv run python scripts/check_release_readiness.py
uv run python scripts/run_golden_evals.py
uv run python scripts/check_mcp_registry_status.py
uv run python scripts/check_directory_presence.py
uv build

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