Prototype · Preview: every server in the official registry, synced every 30 minutes. Reviews and accounts come in a later step.
PopNx MCPMCP servers for people who use AI · explained in plain words

MCP Icon Visual

by n0isyversion 0.1.1i★ –iRuns on your computer
Registry nameio.github.n0isy/mcp-icon-visual

Install

Overview

Description from the maker (original English)

Search, retrieve, compare, and render SVG icons visually for AI agents.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

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: Design, images & audio

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).
  • The registry lists no key to fill in.

You need

  • npmNode.js installed (for the npx 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": {
    "icon-visual": {
      "command": "npx",
      "args": [
        "-y",
        "@br-ai-n/mcp-icon-visual"
      ]
    }
  }
}

Run this in your terminal

claude mcp add icon-visual -- npx -y @br-ai-n/mcp-icon-visual

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "icon-visual": {
      "command": "npx",
      "args": [
        "-y",
        "@br-ai-n/mcp-icon-visual"
      ]
    }
  }
}

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.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "icon-visual": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@br-ai-n/mcp-icon-visual"
      ]
    }
  }
}

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.

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 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account n0isy, 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 Design, images & audioi

  • More stars than 0% of the other 2751 servers in this category
  • Published to the registry more recently than 3% of the other 2751 servers in this category
  • Basic checks equal to or better than 100% of the other 2751 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 MCP Icon Visual 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 (0.1.1) reached the registry 8 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Figma Context MCP, Claude Real Video, Figma MCP Server

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.

Runs on your computer★ 16,165MITUpdated 5 months ago
Install

Claude Real Video

by HUANGCHIHHUNGLeo

Let any LLM watch a video locally — and search everything it has ever watched.

Runs on your computer★ 2,284MITUpdated 2 months ago
Install

Figma’s official server gives AI your design data so it can turn designs into code or describe them accurately. Connects to Figma’s cloud.

Runs in the cloud★ 2,152Updated 13 months ago
Install

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.

mcp-icon-visual

MCP server that gives AI agents the ability to search, retrieve, compare, and render SVG icons. Built with Rust and the rmcp SDK.

Combines local filesystem access with a remote rendering API to resolve SVGs from any source — inline strings, URLs, local files, or Iconify IDs — and render them onto visual comparison grids.

The key insight: every tool returns images. A vision-capable LLM doesn't just get metadata — it *sees* the icons. This turns icon selection from guesswork into a visual conversation.

search_icons("machine learning") — 4x4 grid, cells 0–15, returned as PNG

Agent Pipelines

These tools are designed to be composed by AI agents with vision. Below are three proven patterns.

1. Visual Search & Selection

An agent searches for icons and visually picks the best match — no blind URL selection.

                          ┌─────────────────────────────┐

│ Agent + Vision │ └──────────────┬──────────────┘ │ ┌────────────────┼────────────────┐ v v v search_icons search_icons search_icons "cloud" "upload" "storage" │ │ │ v v v ┌──────────┐ ┌──────────┐ ┌──────────┐ │ 4x4 grid │ │ 4x4 grid │ │ 4x4 grid │ │ (sees) │ │ (sees) │ │ (sees) │ └────┬─────┘ └────┬─────┘ └────┬─────┘ │ │ │ └───────────────┼───────────────┘ v "cells 3, 7, 12 match the project style" │ v render_grid [3 winners] │ v ┌──────────────────┐ │ final comparison │ │ grid (sees) │──> pick best └──────────────────┘

The agent runs multiple searches, looks at each grid, picks candidates by cell number, then renders a final side-by-side comparison to make the choice. Works especially well when the agent has context about the project's visual style.

2. Iterative Icon Editing

An agent modifies SVG code and uses render_grid as a visual diff to track progress across iterations.

         get_svg "mdi/cloud"

│ v ┌───────────┐ │ raw SVG │ │ markup │ └─────┬─────┘ │ ┌────────┴─────────────────────────────────────┐ │ Edit Loop │ │ │ │ Agent modifies SVG code │ │ │ │ │ v │ │ render_grid [original, v1, v2, v3] │ │ │ │ │ v │ │ ┌──────────────────────────────┐ │ │ │ cell 0: original │ │ │ │ cell 1: thicker strokes │ │ │ │ cell 2: rounded corners │ │ │ │ cell 3: filled variant │ │ │ └──────────────┬───────────────┘ │ │ │ │ │ agent sees all versions │ │ decides next edit │ │ │ │ │ └──── loop ──────────────────┘ │ v save final SVG to disk

Each iteration, the agent passes all versions (original + edits) as inline SVGs to render_grid. It sees them side-by-side in one image and decides whether to keep iterating or stop. The original always stays in cell 0 as a reference.

3. Style Matching & Consistency

An agent ensures new icons match the visual style of existing project icons.

  Project icons on disk             Candidate sources

───────────────────── ───────────────── /app/icons/nav-home.svg search_icons "settings" /app/icons/nav-search.svg │ /app/icons/nav-profile.svg v │ picks cells 2, 5, 9 │ │ └──────────┬───────────────────┘ v render_grid [ /app/icons/nav-home.svg, ← cell 0: existing /app/icons/nav-search.svg, ← cell 1: existing /app/icons/nav-profile.svg, ← cell 2: existing candidate_url_1, ← cell 3: candidate candidate_url_2, ← cell 4: candidate candidate_url_3, ← cell 5: candidate " ...custom edit... " ← cell 6: agent's edit ] │ v ┌────────────────────────────────┐ │ grid image: │ │ existing icons vs candidates │ │ (agent sees style match/clash) │ └────────────────┬───────────────┘ │ agent judges: "cell 4 matches stroke weight and corner radius of cells 0-2, cell 3 is too thin, cell 5 wrong fill style" │ v get_svg cell_4_url │ v save to /app/icons/nav-settings.svg

By mixing local file paths and remote sources in a single grid, the agent can visually compare existing project icons against candidates in one shot. The grid becomes a style audit tool.

Combining Pipelines

These patterns compose naturally. A real-world workflow might:

  1. Search across multiple keywords (pipeline 1)
  2. Compare top candidates against existing project icons (pipeline 3)
  3. Edit the closest match to fix style inconsistencies (pipeline 2)
  4. Verify the final icon one more time against the full icon set (pipeline 3)

All driven by a single agent with vision, using three tools.

Tools

search_icons

Semantic icon search. Returns a 4x4 PNG grid (512x512, cells 0-15) and a mapping of cell numbers to SVG URLs.

ParameterTypeDefaultDescription
keywordstring*required*Semantic search query (e.g. "cloud computing", "arrows")
backgroundstring"#FFFFFF"Grid background color, CSS hex
colorstring \nullnullRecolor all icons to this CSS hex color
get_svg

Retrieve raw SVG markup from any source. Returns the complete SVG string for inspection or modification.

ParameterTypeDescription
sourcestringURL, Iconify ID (mdi/cloud), absolute file path, or inline
render_grid

Render 1-16 SVGs onto a 4x4 comparison grid (512x512, cells 0-15). Useful for comparing icon variants side-by-side or previewing local icons alongside search results.

ParameterTypeDefaultDescription
sourcesstring[]*required*1-16 SVG sources (URLs, Iconify IDs, file paths, or inline SVGs)
backgroundstring"#FFFFFF"Grid background color, CSS hex

Source Resolution

All tools share a unified resolution pipeline that classifies sources automatically:

Source typeExampleResolution
Inline SVG ... Returned as-is
URLhttps://mdn.alipayobjects.com/.../originalResolved via API
File path/home/user/icons/logo.svgRead from local disk
Iconify IDmdi/cloud, lucide/homeResolved via API

File paths are detected by prefix (/, ./, ~/) or .svg extension. Everything else without http:///https:// is treated as an Iconify ID.

Grid Format

All grid outputs are 512x512 PNG images with a 4x4 layout (128x128 cells), numbered 0-15 left-to-right, top-to-bottom:

┌────┬────┬────┬────┐

│ 0 │ 1 │ 2 │ 3 │ ├────┼────┼────┼────┤ │ 4 │ 5 │ 6 │ 7 │ ├────┼────┼────┼────┤ │ 8 │ 9 │ 10 │ 11 │ ├────┼────┼────┼────┤ │ 12 │ 13 │ 14 │ 15 │ └────┴────┴────┴────┘

Cell numbers appear at the bottom-left of each cell. Unused cells show the number only.

Installation

npx (recommended)

No build needed. Works on Linux (x64/arm64), macOS (Intel/Apple Silicon), and Windows (x64).

{

"mcpServers": { "icon-visual": { "command": "npx", "args": ["-y", "@br-ai-n/mcp-icon-visual"] } } }

From source

Requires Rust (edition 2024):

cargo build --release --target x86_64-unknown-linux-musl

The release profile produces a fully static binary (musl libc, rustls-tls, LTO, stripped).

Usage

# Default upstream API

./mcp-icon-visual

Custom API endpoint

./mcp-icon-visual --api-base http://localhost:3000

The server communicates over stdio using the MCP protocol.

MCP Client Configuration

Claude Desktop / Cursor
{

"mcpServers": { "icon-visual": { "command": "npx", "args": ["-y", "@br-ai-n/mcp-icon-visual"] } } }

Custom API endpoint
{

"mcpServers": { "icon-visual": { "command": "/path/to/mcp-icon-visual", "args": ["--api-base", "http://localhost:3000"] } } }

Project Structure

src/

├── main.rs # Entrypoint, CLI args, stdio transport ├── server.rs # IconServer + MCP tool routing + response builders ├── api_client.rs # HTTP client for upstream rendering API ├── resolve.rs # Source classification and SVG resolution ├── error.rs # Error types └── tools/ ├── mod.rs ├── search_icons.rs # SearchIconsParams ├── get_svg.rs # GetSvgParams └── render_grid.rs # RenderGridParams

Testing

# Unit tests (source classification)

cargo test

Integration tests (requires network access to icons.buan.me)

cargo test -- --ignored

Dependencies

CratePurpose
rmcpMCP server SDK (tool routing, stdio transport)
tokioAsync runtime
reqwestHTTP client (rustls-tls, no OpenSSL)
serde / serde_jsonSerialization
schemarsJSON Schema generation for tool parameters
clapCLI argument parsing
base64Base64 encoding
tracingLogging
thiserrorError derive macros

License

MIT License

Copyright (c) 2025

Permission is hereby granted,

Only the beginning is shown; read the rest in the repo

Ask PopNx