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shadcn-ui-mcp-server

A mcp server to allow LLMS gain context about shadcn ui component structure,usag

shadcn/ui works by copying component source into your project rather than installing a dependency. That creates a problem: the AI doesn't know what your copy of a component looks like, so it routinely invents props that don't exist.

This server feeds the AI the actual shadcn/ui component source, demos, and dependency graph. Ask for "a data table with filtering" and it works from the real Table and Select definitions, so the generated code actually runs.

GitHub ↗
51
TrustScore
✅ No clear abandonment signal detected; complete security and compatibility checks before production use

Why use it

Good for: frontend developers on shadcn/ui who've been burned by hallucinated props.

Before connecting: it calls the GitHub API to fetch component source, so without a token you'll hit the 60-requests/hour limit. Configure a read-only token.

Before you connect

🔑 Needs an API key / tokenNode.jsDocker

Install / Connect

# No npm package — install from source.
# See the repo's README for the exact command:
# https://github.com/Jpisnice/shadcn-ui-mcp-server

Follow the official README; env and args vary per server.

🛠️ Maintenance weight 30% · 38 pts

📝
Last commit95 days ago
📈
Commits in 90 days0
💬
Recent issues with replies100%
🗃️
Repo statusNormal

📊 Adoption weight 25% · 62 pts

GitHub stars2.9k (90d +0%)
📦
npm weekly downloadsNot npm-distributed
🚀
Releases per month

✅ Usability weight 20% · 20 pts

📋
Official registryNot listed
▶️
Runnable entryNot resolvable
🔍
AuditabilityOpen-source repo, auditable

❤️ Health & community weight 25% · 76 pts

🐛
open issues2
🔀
open PRs
⚖️
LicenseMIT
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Contributors / forks17 / 303

Sources: GitHub API · updated 2026-08-19

90-day trend

⭐ stars +0%

Frequently Asked Questions

Does it support shadcn/ui v4?

It fetches component source across versions; check the repo README for exact coverage.

How is this better than reading the docs?

It provides source-level detail — real props, internal implementation, which other components are used — which is more complete than doc examples and directly consumable by the AI.

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