PixLint
Lint, curate, and prepare computer-vision datasets — right from your AI assistant.
PixLint is an MCP server that gives AI assistants — Claude, Cursor, VS Code, and any MCP client — direct, conversational access to a complete computer-vision dataset toolkit: analyze quality, find duplicates and label errors, clean and curate, split, augment, convert formats, and export to every major training framework.
It runs locally over stdio, or self-hosted on the internet over authenticated HTTP.
Why PixLint
Most dataset tooling is either a paid SaaS or a heavy GUI app. PixLint is a single, open-source, self-hostable server an AI agent can drive end to end — and it does things others keep behind paid tiers:
- 🩺 Dataset Doctor — one call runs a full diagnostic and returns a prioritized, executable fix plan.
- Label-error detection — automatically surface images that are probably mislabeled.
- Natural-language query — "find blurry images with a person on the left", answered over your data.
- Weak-slice discovery — find under-represented or low-quality slices to collect or augment next.
- Curation that writes a new dataset — clean / filter / remap, not just report.
- Auto-labeling with a pretrained detector, and one-command Hugging Face publishing.
Features
103 operations — 67 tools, 23 resources, 13 prompts.
| Category | What you get |
|---|---|
| Load | COCO · VOC · YOLO · KITTI · folder, plus cloud (S3 / GCS / Azure) |
| Analyze | Duplicates · quality (blur/exposure/noise/contrast) · integrity · class distribution · embeddings · semantic search · outliers · health score |
| Data intelligence | Dataset Doctor readiness report · label-error detection · natural-language query · weak-slice / bias discovery |
| Curate | Filter to a subset · clean (corrupt / out-of-bounds / degenerate / duplicates) · remap classes — each produces a new dataset |
| Augment & transform | YOLO/classification/segmentation pipelines · resize · normalize · format conversion |
| Split | Stratified / random / temporal / grouped · k-fold · data-leakage detection |
| Auto-label | Pretrained COCO-80 detector → pre-annotated dataset |
| Export & publish | PyTorch · TensorFlow · Ultralytics · HDF5 · WebDataset · FiftyOne · CVAT · LabelMe · Hugging Face Hub |
| Pipelines | Compose multi-step workflows and reuse pre-built templates |
Quick Start
1. Install
pip install pixlint
Optional extras add heavier capabilities:
pip install "pixlint[torch]" # embeddings, auto-labeling, label-error detection
pip install "pixlint[huggingface]" # Hugging Face export + publishing
pip install "pixlint[all]" # everything
2. Connect your AI assistant
Claude Desktop — claude_desktop_config.json:
{
"mcpServers": {
"pixlint": {
"command": "pixlint",
"env": { "CV_DATA_DIR": "/path/to/your/datasets" }
}
}
}
Cursor / VS Code — .cursor/mcp.json or .vscode/mcp.json:
{
"mcpServers": {
"pixlint": {
"command": "pixlint",
"env": { "CV_DATA_DIR": "/path/to/your/datasets" }
}
}
}
CV_DATA_DIR is the directory PixLint is allowed to read datasets from.
3. Just ask
"Load my dataset at
/data/coco_person, give it a readiness report, then clean it and export for YOLO."
Your assistant calls the right PixLint tools in sequence — diagnose, clean, split, export — and hands back a training-ready dataset.
Security
PixLint touches the filesystem and can be exposed to a network, so protections run on every tool call:
- Paths are confined to your configured data directory (reads and writes).
- Credentials come only from environment variables, never tool inputs.
- Per-call rate limiting, concurrency limits, and audit logging.
- Decompression-bomb protection on image decode.
- Optional bearer-token authentication for the HTTP transport.
See the Security Guide for the full threat model and the recommended production checklist.
Documentation
| Guide | Description |
|---|---|
| Getting Started | Installation, configuration, first steps |
| MCP Client Setup | Claude, Cursor, VS Code, and remote/HTTP hosting |
| API Reference | All 67 tools with parameters |
| Security Guide | Threat model, configuration, hosting |
| Pipeline Templates | Pre-built and custom pipelines |
Runnable scripts live in examples/. See CHANGELOG.md for release notes.
License
PixLint is source-available under the PolyForm Strict License 1.0.0 — see LICENSE. You may use it for permitted (noncommercial) purposes; commercial use, redistribution, or modification requires a separate license from the copyright holder. Contributions are welcome via pull request.
mcp-name: io.github.amitsingh-24/pixlint