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Pixlint
Overview
Description from the maker (original English)
Lint, curate & prepare computer-vision datasets from your AI assistant — MCP server, 67 tools
PopNx MCP’s three pillars
These three are never merged into one score: opinions, measurements and popularity are different things.
- User voiceiNo reviews yetReviews open together with accounts in a later step
- Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
- UsageiVery new↓ 18/wk · ★ –
What you can do with it
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
- Connection: Runs on your computer
- Transport: stdio
- Category: Databases & datasets
Install
Pick the AI app you use, then copy the settings.
Before you install
- Basic checks: 1 of 1 passed
- Runs on your computer with your user’s permissions (files, network, apps).
- You fill in yourself:
CV_DATA_DIR,CV_WORKSPACE
You need
- PyPIPython’s uv installed (for the uvx command)
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"pixlint": {
"command": "uvx",
"args": [
"pixlint"
],
"env": {
"CV_DATA_DIR": "your-value",
"CV_WORKSPACE": "your-value"
}
}
}
}Run this in your terminal
claude mcp add pixlint --env CV_DATA_DIR=your-value --env CV_WORKSPACE=your-value -- uvx pixlintFill in the empty values before running it.
Or copy the settings yourself
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"pixlint": {
"command": "uvx",
"args": [
"pixlint"
],
"env": {
"CV_DATA_DIR": "your-value",
"CV_WORKSPACE": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Or copy the settings yourself
Paste into .vscode/mcp.json in your project
{
"servers": {
"pixlint": {
"type": "stdio",
"command": "uvx",
"args": [
"pixlint"
],
"env": {
"CV_DATA_DIR": "your-value",
"CV_WORKSPACE": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
Values you fill in
CV_DATA_DIRenvAbsolute path to the directory PixLint may read datasets from.CV_WORKSPACEenvAbsolute path for outputs (exports, curated datasets).
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
What it can do
Runs on your computer; the tool list appears in your AI app after you install it.
Basic checksi
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 9, 2026
- ✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account amitsingh-24, which owns the repo
- … Not checked yet
Still maintained
We have not read this repo from GitHub yet
- … Not checked yet
Has a licence
We have not read this repo from GitHub yet
- – Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
- – Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
- … Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints
Compared with others in Databases & datasetsi
- More stars than 0% of the other 1152 servers in this category
- Published to the registry more recently than 20% of the other 1152 servers in this category
- Basic checks equal to or better than 100% of the other 1152 servers in this category
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
Common questions
Is Pixlint safe?
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
What keys or codes does it need?
The registry does not list a required key, though some servers ask you to sign in on first use.
Does it run on my computer or in the cloud?
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
Is it still maintained?
The GitHub repo last changed –, and the latest version (1.1.0) reached the registry 4 months ago.
Which apps can use it?
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
What can I use instead?
In the same category: Supabase, MongoDB MCP Server, Elasticsearch MCP Server
Alternatives
Servers in the same category from other makers, sorted by stars.
Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
README from the maker
Open the README
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
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 pixlintOptional extras add heavier capabilities:
pip install "pixlint[torch]" # embeddings, auto-labeling, label-error detectionpip 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