shadcn-ui-mcp-server
A mcp server to allow LLMS gain context about shadcn ui component structure,usage and installation,compaitable with react,svelte 5,vue & React Native
Runssource: GitHubTypeScriptMITcommit 45d615f0950b
TypeScript, MIT licensed. The project labels itself: ai, expo, mcp, modelcontextprotocol, nextjs, npm, npm package and npx.
shadcn-ui-mcp-server runs. An Argusic agent installed it in 0.5 minutes and hit no errors on a clean machine with no GPU, and the whole session was recorded.
The shadcn-ui-mcp-server builds from source, starts in both stdio and SSE modes, responds to MCP protocol requests, and fetches real component data from the shadcn-ui/ui GitHub repository without any modifications or mocks.
What the agent ended up with on a clean machine, in its own words. How this is measured
Measured by Argusic on a fresh machine every time. Every number links to its evidence. Argusic Score 100 of 100 (the mean of this project's run scores).
At a glance
Subject data from GitHub, linked at the top of this page, refreshed . Test data by Argusic (CC BY 4.0); every number links to a run page with the full log, the recording, and their sha256 hashes.
What was measured
From the run that decided this verdict. Every line is a recorded value, not an estimate.
- Install time
- under a minute
- Cold machine to finish
- 5 minutes
- Errors hit and fixed
- 0 hit, 0 fixed with no human help
- How the result was proved
- MCP stdio protocol: initialize returned capabilities, tools/list returned 10 tools, tools/call get_component(button) returned real Radix UI v4 source code from GitHub API. SSE mode: curl /health returned 200 with status healthy. test-package.sh: all 8 tests passed.
- Model tokens used
- 48,117
- Exact commit tested
- 45d615f0950b
- Container image
- sha256:cdd920bce783
What was not measured
The other half, so you can see exactly how far this evidence goes. A gap is printed as a gap and never filled in.
- Peak GPU memory
- this ran on a CPU machine, so there was no GPU to sample
- Behaviour over a long run
- each attempt uses the smallest documented settings and a fixed time budget, so this measures whether it runs, not how it holds up under load
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
exec
/bin/sh -lc 'cd /work/repo && echo "Total wall time for entire task:"; date +%s' in /work/repo
succeeded in 0ms:
Total wall time for entire task:
1790904436
The project installed, built, and ran without any errors. No fixes were needed. Here's the final re…
exec
/bin/sh -lc "echo 'ARGUSIC_RESULT:{\"install_succeeded\":true,\"launch_succeeded\":true,\"install_m…
succeeded in 0ms:
2026-10-02T01:27:24.928581Z ERROR codex_core::session: failed to record rollout items: thread 01a0f…
tokens used
48,117Replay the whole session, every command from a clean machine to this point.
How it was tested
One agent, one container, no human help. It installs the project the way its own documentation says to, runs it, and fixes what breaks. Everything below is recorded as it happened: the terminal session, the log and the exact commit. The full method.
Strengths and limits
Measured facts, not opinions. How this is written.
What went well
- Reached a running state on a clean machine, with the session recorded.
- Installed in 0.5 minutes, faster than the median of the 75 comparable projects Argusic has measured.
- Nothing broke on the way: zero errors between clone and running.
- Ran without a GPU, so it does not need one to start.
- Licensed MIT, as reported by its host.
What did not, or is not known
- Measured once so far. A single attempt shows it can run, not that it runs reliably.
Of the 76 ai projects Argusic has installed and timed, shadcn-ui-mcp-server was the 7th fastest to reach a running state, and 56 of 76 reached one at all.
What it is a reasonable choice for
- Trying it on a laptop or a small server: it reached a running state without a GPU.
- A quick evaluation: it was running 0.5 minutes after the clone.
- Following its own README: the documented path worked with nothing to fix.
Also tested, in the same area
Every one of these was installed and run by Argusic on a clean machine. Nothing appears here that was not tested.
Run history
Topics (from GitHub)
aiexpomcpmodelcontextprotocolnextjsnpmnpm-packagenpxreactreact-nativeshadcn-uisveltesveltekittstypescriptuivuevuejswebdev
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/shadcn-ui-mcp-server)Questions
- Does shadcn-ui-mcp-server run?
- Yes. shadcn-ui-mcp-server runs. Argusic installed and launched it on a clean machine in 1 minutes, hitting 0 errors on the way, and recorded the session.
- How did Argusic test shadcn-ui-mcp-server?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit 45d615f0950b. One attempt is recorded, and the full method is on the methodology page.
- What did it cost to test shadcn-ui-mcp-server?
- The run that produced this verdict cost $0.03: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does shadcn-ui-mcp-server take to install?
- 0.5 minutes in the run that decided this verdict, from a clean machine to a running state. That is one measured attempt, not an average.
- Does shadcn-ui-mcp-server need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing shadcn-ui-mcp-server?
- Nothing did, in the recorded run: zero errors between clone and running.
- How does shadcn-ui-mcp-server compare with the alternatives?
- Of the 76 ai projects Argusic has installed and timed, shadcn-ui-mcp-server was the 7th fastest to reach a running state, and 56 of 76 reached one at all.
- Where is the evidence for shadcn-ui-mcp-server?
- The recorded run is on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.