taroify

Taroify MCP is a dedicated model context protocol service that connects the Taroify UI component library (for mobile mini-programs) with a large language model. It enables the model to directly retrieve component and API data from documentation, enabling context-aware responses when developers inquire about Taroify components or usage. You can invoke the MCP tool via the npx approach shown in the README, which fetches and runs the @taroify/mcp package on demand without a separate install. This makes it easy to query component details, API signatures, and usage patterns in real time as you build or explore applications. The MCP toolset is designed to work with the Cursor MCP ecosystem; you can reference the Cursor MCP guide for how to issue context-based queries and integrate the results into your agent workflows.

Prerequisites:

• Node.js (v14+ recommended; check with node -v)
• Internet access to fetch the MCP package via npx

installation steps:

1. Ensure Node.js is installed. If not, download and install from https://nodejs.org
2. Open a terminal and verify Node.js and npm are available:
   • node -v
   • npm -v
3. Run the MCP server using npx (no persistent install required):

   npx -y @taroify/mcp
   

4. The command will fetch the @taroify/mcp package and start the MCP service for use with your agent. If you prefer to preload, you can also install the package directly with npm:

   npm install -g @taroify/mcp
   

   and then start it according to the package's run instructions (usually via npx or a bin script).
5. When running in scripts or deployments, reference the provided mcp_config to integrate with your agent.

Tips and common issues:

• The MCP relies on network access to fetch the package when using npx. If you are behind a proxy, configure npm/npx proxy settings or run within an environment with network access.
• No environment variables are strictly required for basic operation. If you later add environment-specific configuration, you can supply them under the env field, e.g., { "ENV_VAR": "value" }.
• For reproducible environments, prefer a local install of @taroify/mcp or pin the version in your CI to avoid breaking changes.
• If you encounter issues with missing documentation or API data, verify that the MCP package version aligns with the Taroify documentation you’re using and consult Cursor MCP usage guidelines for integrating tool outputs into your agent workflows.