birdseyevue/daisyui-mcp

Description and use

**Brief Summary (2‑3 sentences)**
birdseyevue/daisyui-mcp is an open‑source, token‑friendly MCP (Model Context Protocol) server that serves up DaisyUI component documentation from the public llms.txt file. Written in Python, it provides a lightweight API/SDK/CLI for AI agents to query real‑world UI component data, making it easy to integrate DaisyUI knowledge into conversational or autonomous workflows. With recent commits, 75 GitHub stars, and active community interest, it’s positioned as a ready‑to‑pilot tool for AI‑augmented front‑end development.

**Value**

• **Standardised AI‑tool bridge** – By implementing the MCP spec, the server gives AI assistants a uniform way to retrieve structured component docs, reducing ad‑hoc scraping or hard‑coded prompts.
• **Token‑friendly** – The server returns concise, pre‑processed snippets, keeping LLM token usage low while still delivering the needed design information.
• **Accelerates AI‑driven UI work** – Developers can quickly build agents that suggest, validate, or generate DaisyUI code, shortening the design‑to‑implementation cycle.

**Practical Adoption Path**

1. **Clone & install** – git clone https://github.com/birdseyevue/daisyui-mcp && pip install -r requirements.txt.
2. **Run the MCP server** – python -m daisyui_mcp starts a local HTTP endpoint (default /query).
3. **Integrate** – Point your LLM‑orchestrator (e.g., LangChain, CrewAI, or a custom agent) to the endpoint using the MCP client library or a simple REST call.
4. **Extend** – If you need extra DaisyUI versions or custom component metadata, edit the llms.txt source or add a plug‑in module; the server reloads automatically.
5. **Deploy** – Containerise with the provided Dockerfile or deploy to a cloud function for production‑scale access.

**Production Readiness**

• **Activity & community** – Last commit on 2026‑07‑12, 75 stars, 16 forks, and a clear Python codebase suggest healthy maintenance.
• **Stability** – The MCP interface is well‑defined, and the server exposes both REST and SDK entry points, easing integration testing.
• **Security & licensing** – No immediate metadata risks, but a final audit of the MIT‑style license, dependency vulnerabilities, and maintainer responsiveness is advisable before a full production rollout.

Overall, the project is mature enough for a pilot or internal service, with a straightforward path to scale once the security and governance checks are completed.

Use in a workflow

birdseyevue/daisyui-mcp helps connect AI assistants to real tools and data through a standard protocol.

• connect AI agents to tools
• ship Model Context Protocol servers
• standardize integrations

Limitations and review notes

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.