# pixlint
**Category:** 💻 Developer Tools
**Repository:** https://github.com/amitsingh-24/PixLint
**Views:** 0
**Installs:** 0
**Upvotes:** 0
**Directory Page:** https://allmcps.com/mcp/pixlint
## Description
Lint, curate & prepare computer-vision datasets from your AI assistant — MCP server, 67 tools
## Claude Desktop Quick Installation
Heuristic fallback — verify the package name and runner against the repository README before running it. Uses `npx` (confidence: low):
```json
"mcpServers": {
"pixlint": {
"command": "npx",
"args": ["-y","pixlint"]
}
}
```
## Documentation & README
# PixLint
**Lint, curate, and prepare computer-vision datasets — right from your AI assistant.**
[](https://python.org)
[](LICENSE)
[](https://modelcontextprotocol.io)
PixLint is an [MCP](https://modelcontextprotocol.io) 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
```bash
pip install pixlint
```
Optional extras add heavier capabilities:
```bash
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`:
```json
{
"mcpServers": {
"pixlint": {
"command": "pixlint",
"env": { "CV_DATA_DIR": "/path/to/your/datasets" }
}
}
}
```
**Cursor / VS Code** — `.cursor/mcp.json` or `.vscode/mcp.json`:
```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](docs/security.md) for the full threat model and the recommended production checklist.
---
## Documentation
| Guide | Description |
|-------|-------------|
| [Getting Started](docs/getting_started.md) | Installation, configuration, first steps |
| [MCP Client Setup](docs/mcp_client_setup.md) | Claude, Cursor, VS Code, and remote/HTTP hosting |
| [API Reference](docs/api_reference.md) | All 67 tools with parameters |
| [Security Guide](docs/security.md) | Threat model, configuration, hosting |
| [Pipeline Templates](docs/pipeline_templates.md) | Pre-built and custom pipelines |
Runnable scripts live in [`examples/`](examples/). See [CHANGELOG.md](CHANGELOG.md) for release notes.
---
## License
PixLint is source-available under the **PolyForm Strict License 1.0.0** — see [LICENSE](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