lotus-wisdom

Lotus Wisdom MCP server provides a structured problem-solving toolset based on the Lotus Sutra-inspired wisdom framework. It includes a lotuswisdom tool for iterative contemplation across multiple tags (begin, open, direct, meditate, integrate, express, etc.) and a lotuswisdom_summary tool to review the journey so far. Users submit a problem, and the server guides the model through a transparent, colored, domain-aware reasoning process with optional meditation pauses to allow insights to surface. Use lotuswisdom to explore diverse perspectives and then rely on lotuswisdom_summary to gauge the progress of the journey, including the path across wisdom domains and a summary of steps taken. This setup is designed to produce well-structured, multi-faceted thinking before presenting final insights.

Prerequisites:

• Node.js 18+ and npm (or npx availability in your environment)
• Access to a shell/terminal

Installation steps:

1. Ensure Node.js is installed: node -v and npm -v
2. Install and run the MCP server via npx (one-liner):

# Claude Code
claude mcp add lotus-wisdom -- npx -y lotus-wisdom-mcp

# Codex CLI (OpenAI)
codex mcp add lotus-wisdom -- npx -y lotus-wisdom-mcp

# Gemini CLI (Google)
gemini mcp add lotus-wisdom npx -y lotus-wisdom-mcp

3. Start using the server by invoking the configured MCP entry as shown in your client/CLI, which will connect to the lotus-wisdom-mcp via npx.

Optional: If you prefer a direct npm package approach, you can install the package globally and run it, depending on your tooling.

Tips and notes:

• The server relies on the lotuswisdom tool for step-by-step contemplation and the lotuswisdom_summary tool for journey summaries.
• The visualization and domain tracking occur in the console output for developers; end-users will interact with the provided tools via their MCP client.
• If you encounter issues with npx caching or package resolution, clear npm/npx caches or reinstall Node.js to ensure compatibility with Node 18+.
• Environment variables are not strictly required for basic operation, but you may want to expose API keys or model-specific configuration through env when integrating with broader systems.
• This MCP is designed to work across model versions with consistent functionality; if you’re integrating into a larger workflow, consider using the summary endpoint after each major step to verify alignment with your goals.