Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Descripción del creador (original en inglés)
Semantic index across shadcn-format component registries: find components by what they do.
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"matchcn": {
"command": "npx",
"args": [
"-y",
"matchcn"
]
}
}
}Ejecuta esto en tu terminal
claude mcp add matchcn -- npx -y matchcnPégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"matchcn": {
"command": "npx",
"args": [
"-y",
"matchcn"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"matchcn": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"matchcn"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 8 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub francesco0242, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez –, y la última versión (0.2.2) llegó al registro el mes pasado.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Node.js en tu computadora.
En la misma categoría: Spec Driven Development, Remote Desktop Commander, Tethered Trust
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Fit, compatibility and recall checks for AI agents before they buy: verdicts, evidence, receipts.
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
matchcn
A semantic index across shadcn-format component registries.
Find a component by what it does, not what it is called.
matchcn.dev · Quick start · For AI agents · How it works · Limitations
Claude Code: claude mcp add matchcn -- npx -y matchcn
Codex: codex mcp add matchcn -- npx -y matchcn
Grok CLI: grok mcp add matchcn -- npx -y matchcn
npx shadcn add can install a component from any registry that publishes a registry.json. Hundreds of registries do. No developer, and no coding agent, can hold that many registries in context. Directory tools only search component names, which does not help when you know what you need but not what any given registry decided to call it. "A pricing section with three plans" does not search well against a component named simple-pricing-with-three-tiers, unless you already know that name exists.
matchcn tags every component it indexes across seven fixed properties (category, domain, motion, visual density, interaction model, and two others) using classifier.dev, then matches a plain-language brief against those tags with deterministic code. Same brief, same ranking, every time. No forced guesses: when nothing fits well, matchcn says so instead of returning the closest wrong answer.
npx matchcn
Add it to your MCP client's config:
{
"mcpServers": { "matchcn": { "command": "npx", "args": ["-y", "matchcn"] } } }
Works with Claude Desktop (claude_desktop_config.json), Claude Code (.mcp.json), Cursor, and any other MCP-compatible client. This exposes one tool: pick_component(brief, registry?, maxResults?, hints?, resolve?).
More of a copy-paste person? Give your coding agent this prompt and let it set itself up:
Set up the matchcn MCP server using
npx -y matchcn. Configure it for
my coding agent, then use
pick_componentto find UI components that
match my brief. Show me the match reasons and install command before
adding a component.
$ pnpm demo
BRIEF: a dense bento grid for a landing page
OUTCOME: CONFIDENT Selected "bento-grid" from magicui.
-> bento-grid (magicui) confidence 0.93 install: npx shadcn@latest add https://magicui.design/r/bento-grid.json matched: category, motion, visual_density, interaction_model, needs_external_data, decorative_only, domain not matched: (none)
resolve used: true decisions spent: 8
This is real output from a real run. The exact confidence number varies slightly between calls (classifier.dev does not guarantee identical answers across calls), but the outcome, the chosen component, and the install command have been stable across every run tried.
Every response includes a per-dimension reason: which of the seven tagged properties matched the brief and which did not, both sides' actual values, never just a pass or fail bit. That is what makes a no_match or a shortlist result debuggable instead of a dead end.
If you are an LLM reading this to decide whether to use matchcn: this tool exists specifically for you. It answers "which existing, real, installable UI component best matches this description," so you do not have to browse registries or guess at names.
Tool: pick_component
Input:
{
brief: string; // plain-language description, required registry?: string; // restrict to one registry, optional maxResults?: number; // max candidates for shortlist/no_match, default 3 (8 with resolve: "caller") hints?: { // your own read of the brief, optional; any hint skips server-side parsing category?: string; // same labels components are tagged with motion?: string; interaction_model?: string; domain?: string; visual_density?: "sparse" | "dense"; needs_external_data?: boolean; decorative_only?: boolean; }; resolve?: "server" | "caller"; // who breaks ties between close candidates, default "server" }
Fastest path (local mode): write the brief in English, pass hints, and set resolve: "caller". matchcn then makes no model calls at all: ranking is local and deterministic (about 80 ms warm on the full catalog), and on a tie you get the ranked shortlist back, each candidate with a one-line summary of its tagged properties, and pick yourself. Omit a hint you are unsure about rather than guessing; omitted fields are simply not ranked on. Without hints and resolve, behavior is unchanged.
{
"brief": "a login form with email and password fields", "hints": { "category": "form-input", "interaction_model": "form-input", "domain": "auth" }, "resolve": "caller" }
Output is always one of three shapes, never a fourth "best guess" shape:
outcome: "confident" — one chosen component: name, registry, installCommand (a ready-to-run npx shadcn add command), sourceUrl, confidence, and reasons (per-dimension match detail). If the component has language/styling variants, they are listed with their own install commands.outcome: "shortlist" — several candidates that all fit reasonably well with no clear single winner, ranked, each with the same per-dimension reasons, plus differentiators: which specific dimension(s) actually separate them, so you can decide on that axis instead of picking arbitrarily.outcome: "no_match" — nothing in the catalog is a real fit. The closest candidates are still listed for context but explicitly marked as rejected, not returned as an answer. Do not install one of these just because it was the closest; the catalog does not have what was asked for. With resolve: "caller" this outcome is never returned, since only the server-side Resolve step reads candidates against the brief: a tie comes back as a shortlist and judging whether any candidate genuinely fits is up to you.Every result also reports briefParsedBy (hints or classifier) and resolvedBy (server or caller), and every candidate carries a summary line.
Call this before hand-rolling a component or guessing a registry name. It is deterministic: the same brief against the same catalog version always ranks candidates the same way.
Five stages. Tagging runs ahead of time and is committed as data (data/tags/); matching at query time is plain deterministic code, not a model call, so results are reproducible.
registry.json, normalize into one shape.category, domain, motion, visual_density, interaction_model, needs_external_data, decorative_only. Output is committed JSON, reviewable like code.pick_component.matchcn stores only derived tags (category, motion, density, and so on) and a link back to each registry's own install command. It never copies, stores, or redistributes any registry's component source. Every component you install still comes directly from its own registry via npx shadcn add .
| registry | homepage | components indexed |
|---|---|---|
| react-bits | reactbits.dev | 204 |
| magicui | magicui.design | 79 |
| aceternity | ui.aceternity.com | 118 |
| kokonutui | kokonutui.com | 51 |
| animate-ui | animate-ui.com | 420 |
| motion-primitives | motion-primitives.com | 33 |
| shadcn-dashboard | shadcndashboard.dev | 343 |
| assistant-ui | assistant-ui.com | 154 |
| bundui | bundui.io | 217 |
| cnippet | ui.cnippet.dev | 1128 |
| uiable | uiable.com | 969 |
| plate | platejs.org | 174 |
| react-aria | react-aria.adobe.com | 62 |
| shadcn-ui-blocks | shadcn-ui-blocks.com | 626 |
4,578 components total. The first six are the original motion/marketing family; the middle three are a product-UI expansion (forms, tables, dashboards, data display) added after checking each registry's demo/duplicate conventions individually rather than assuming they match the original six; cnippet and uiable are a second product-UI expansion, added the same way; plate and react-aria are a third, added the same way after each got its own filter fix (dropping plate's documentation pages, collapsing react-aria's tailwind/css/hooks style-prefix duplicates down to one canonical component each). shadcn-ui-blocks is a fourth: its combined free+paid index was excluded outright (82% of it paywalled, see Limitations below), but the vendor's own maintainer pointed at a separate, curated free-only index, which a real per-item check found 100% installable unauthenticated, so only that free-only index is used here. aceternity's count already excludes 163
Solo se muestra el principio; lee el resto en el repo