Lotus Wisdom Mcp

🪷 Lotus Wisdom MCP Server

An MCP server implementation that provides a tool for problem-solving using the Lotus Sutra's wisdom framework, combining analytical thinking with intuitive wisdom.

Available at: https://lotus-wisdom-mcp.linxule.workers.dev/mcp

Features

• Multi-faceted problem-solving approach inspired by the Lotus Sutra
• Step-by-step thought process with different thinking techniques
• Meditation pauses to allow insights to emerge naturally
• Interactive visualization via MCP ext-apps (Claude Desktop, Cursor, ChatGPT) — adapts to the host light/dark theme and is keyboard-accessible
• MCP Prompts (contemplate, deep-inquiry) for one-step guided contemplative sessions
• Structured tool output (structuredContent + outputSchema) alongside the text response
• Tracks both tag journey and wisdom domain movements
• Available as a local stdio package (npx) or a hosted remote Connector
• Final integration of insights into a clear response

Background

This MCP server was developed from the Lotus OS prompt, which was designed to implement a cognitive framework based on the Lotus Sutra. The MCP server format makes this framework more accessible and easier to use with Claude and other AI assistants.

Note: The original prompt framework may work less effectively with newer Claude models, but this MCP server implementation provides consistent functionality across model versions.

Implementation Details

The server implements a structured thinking process using wisdom domains inspired by the Lotus Sutra:

Wisdom Domains and Tags

The server organizes thoughts using wisdom domains (all valid values for the tag input parameter):

• Entry (🚪): begin
• Begin your journey here - receives the full framework before contemplation starts

• Skillful Means (🔆): upaya, expedient, direct, gradual, sudden
• Different approaches to truth - sometimes direct pointing, sometimes gradual unfolding

• Non-Dual Recognition (☯️): recognize, transform, integrate, transcend, embody
• Aspects of awakening to what's already present - recognition IS transformation

• Meta-Cognitive (🧠): examine, reflect, verify, refine, complete
• The mind watching its own understanding unfold

• Process Flow (🌊): open, engage, express
• A natural arc that can contain any of the above approaches

• Meditation (🧘): meditate
• Pausing to let insights emerge from stillness

Thought Visualization

In clients that support MCP ext-apps, each step renders inline as an interactive "Living Trace" (see [Interactive Visualization](#interactive-visualization-ext-apps) below). For every client, each step also returns:

• Journey tracking showing both the tag path and the wisdom-domain movements
• Domain-specific labels and the current contemplation text
• Structured output (structuredContent + outputSchema) for programmatic consumers

Note: The local stdio server can emit per-step trace lines to its console (stderr) when run with LOTUS_DEBUG=true, helping developers follow the thinking process.

Process Flow

1. The user submits a problem to solve
2. The model begins with tag='begin' to receive the full framework
3. The model continues with contemplation tags (open, examine, integrate, etc.)
4. Each thought builds on previous ones and may revise understanding
5. The tool tracks both the tag journey and wisdom domain movements
6. Meditation pauses can be included for clarity
7. When status='WISDOM_READY' is returned, the tool's work is complete
8. The model then expresses the final wisdom naturally in its own voice

Available Tools

lotuswisdom

A tool for problem-solving using the Lotus Sutra's wisdom framework, with various approaches to understanding.

Begin your journey with tag='begin' - this returns the full framework (philosophy, domains, guidance) to ground your contemplation. Then continue with the other tags.

Inputs:

• tag (string, required): The current processing technique (must be one of the tags listed above)
• content (string, required): The content of the current processing step
• stepNumber (integer, required): Current number in sequence
• totalSteps (integer, required): Estimated total steps needed
• nextStepNeeded (boolean, required): Whether another step is needed
• isMeditation (boolean, optional): Whether this step is a meditative pause
• meditationDuration (integer, optional): Duration for meditation in seconds (1-10)
• previousJourney (string, optional): The journey string from a previous response, e.g. "begin → open → examine". Lets the AI carry journey continuity forward in stateless clients (such as the remote Worker), where the server keeps no session state.

Returns: both a JSON text block and matching structuredContent (validated against the tool's outputSchema):

• Processing status with current step information, wisdom domain, and journey tracking
• FRAMEWORK_RECEIVED status (with the full framework) on the first begin step
• MEDITATION_COMPLETE status for meditation steps
• WISDOM_READY status when the contemplative process is complete

The tool also declares behavioral annotations — readOnlyHint, idempotentHint, destructiveHint: false, openWorldHint: false — so hosts can treat it as a safe, side-effect-free call.

lotuswisdom_summary

Get a summary of the current contemplative journey.

Inputs:

• previousJourney (string, optional): The journey string from a previous response, used to reconstruct the summary in stateless clients.

Returns:

• Journey length
• Domain journey showing movement between wisdom domains
• Summary of all steps with their tags, domains, and brief content

MCP Prompts

The server registers two prompts that scaffold a guided contemplative session (surfaced as slash commands or prompt pickers in clients that support MCP Prompts):

• contemplate — argument question: opens a single-question contemplation, instructing the model to start with tag='begin', iterate, and speak the wisdom only once status='WISDOM_READY'.
• deep-inquiry — argument topic: begins a longer inquiry that moves deliberately across the wisdom domains (process → meta-cognitive → non-dual → meditation).

Usage

The Lotus Wisdom tool is designed for:

• Breaking down complex problems requiring multi-faceted understanding
• Questions that benefit from both direct and gradual approaches
• Problems where apparent contradictions need integration
• Situations requiring both analytical and intuitive understanding
• Tasks that benefit from meditative pauses to allow insight
• Questions containing their own inherent wisdom

Example Usage

Here's how a conversation with Claude might flow when using the Lotus Wisdom MCP server:

User: "Help me understand the relationship between freedom and responsibility."

Claude would begin the journey with tag='begin' to receive the framework, then continue:

1. First, enter the contemplative space:

{
  "tag": "begin",
  "content": "Entering contemplation on freedom and responsibility.",
  "stepNumber": 1,
  "totalSteps": 6,
  "nextStepNeeded": true
}

→ Returns FRAMEWORK_RECEIVED with full framework

1. Then, establish an opening thought:

{
  "tag": "open",
  "content": "The question explores the relationship between freedom and responsibility, which contain an apparent tension but also deep connection.",
  "stepNumber": 2,
  "totalSteps": 6,
  "nextStepNeeded": true
}

1. Engage with different perspectives:

{
  "tag": "direct",
  "content": "Freedom and responsibility are two sides of the same coin. True freedom isn't absence of constraints but the capacity to choose our response within constraints.",
  "stepNumber": 3,
  "totalSteps": 6,
  "nextStepNeeded": true
}

1. Perhaps take a meditation pause:

{
  "tag": "meditate",
  "content": "Contemplating how freedom without responsibility becomes chaos, and responsibility without freedom becomes oppression.",
  "stepNumber": 4,
  "totalSteps": 6,
  "nextStepNeeded": true,
  "isMeditation": true
}

1. Integrate the understanding:

{
  "tag": "integrate",
  "content": "Freedom and responsibility mutually enable each other. Our freedom to choose gives rise to our responsibility for what we choose, and our willingness to take responsibility expands our freedom.",
  "stepNumber": 5,
  "totalSteps": 6,
  "nextStepNeeded": true
}

1. Express the final understanding:

{
  "tag": "express",
  "content": "The paradox resolves when we see that authentic freedom includes responsibility as its natural expression.",
  "stepNumber": 6,
  "totalSteps": 6,
  "nextStepNeeded": false
}

When the tool returns status: 'WISDOM_READY', Claude then speaks the final wisdom naturally, integrating all the insights from the contemplative journey.

Installation

[](https://smithery.ai/server/lotus-wisdom-mcp)

Install via Smithery for one-click setup, or follow the manual instructions below.

Requires Node.js 18+. The server runs locally via npx.

CLI Install (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

Claude Desktop

Add to your claude_desktop_config.json:

| OS | Config path | |----|-------------| | macOS | ~/Library/Application Support/Claude/claude_desktop_config.json | | Windows | %APPDATA%\Claude\claude_desktop_config.json | | Linux | ~/.config/Claude/claude_desktop_config.json |

{
  "mcpServers": {
    "lotus-wisdom": {
      "command": "npx",
      "args": ["-y", "lotus-wisdom-mcp"]
    }
  }
}

VS Code

Add to .vscode/mcp.json (workspace) or open Command Palette > MCP: Open User Configuration (global):

{
  "servers": {
    "lotus-wisdom": {
      "command": "npx",
      "args": ["-y", "lotus-wisdom-mcp"]
    }
  }
}

> Note: VS Code uses "servers" as the top-level key, not "mcpServers". Other VS Code forks (Trae, Void, PearAI, etc.) typically use this same format.

Cursor

Add to ~/.cursor/mcp.json (global) or .cursor/mcp.json (project):

{
  "mcpServers": {
    "lotus-wisdom": {
      "command": "npx",
      "args": ["-y", "lotus-wisdom-mcp"]
    }
  }
}

Windsurf

Add to ~/.codeium/windsurf/mcp_config.json (Windows: %USERPROFILE%\.codeium\windsurf\mcp_config.json):

{
  "mcpServers": {
    "lotus-wisdom": {
      "command": "npx",
      "args": ["-y", "lotus-wisdom-mcp"]
    }
  }
}

Cline

Open MCP Servers icon in Cline panel > Configure > Advanced MCP Settings, then add:

{
  "mcpServers": {
    "lotus-wisdom": {
      "command": "npx",
      "args": ["-y", "lotus-wisdom-mcp"]
    }
  }
}

Cherry Studio

In Settings > MCP Servers > Add Server, set Type to STDIO, Command to npx, Args to -y lotus-wisdom-mcp. Or paste in JSON/Code mode:

{
  "lotus-wisdom": {
    "name": "Lotus Wisdom",
    "command": "npx",
    "args": ["-y", "lotus-wisdom-mcp"],
    "isActive": true
  }
}

Witsy

In Settings > MCP Servers, add a new server with Type: stdio, Command: npx, Args: -y lotus-wisdom-mcp.

Codex CLI (TOML config)

Alternatively, edit ~/.codex/config.toml directly:

[mcp_servers.lotus-wisdom]
command = "npx"
args = ["-y", "lotus-wisdom-mcp"]

Gemini CLI (JSON config)

Alternatively, edit ~/.gemini/settings.json directly:

{
  "mcpServers": {
    "lotus-wisdom": {
      "command": "npx",
      "args": ["-y", "lotus-wisdom-mcp"]
    }
  }
}

Windows

On Windows, npx requires a shell wrapper. Replace "command": "npx" with:

{
  "command": "cmd",
  "args": ["/c", "npx", "-y", "lotus-wisdom-mcp"]
}

For CLI tools on Windows:

claude mcp add lotus-wisdom -- cmd /c npx -y lotus-wisdom-mcp
codex mcp add lotus-wisdom -- cmd /c npx -y lotus-wisdom-mcp

ChatGPT

ChatGPT only supports remote MCP servers over HTTPS. Use Smithery or connect directly to the hosted instance below via ChatGPT Settings > Connectors.

Remote (hosted)

A public instance is available at https://lotus-wisdom-mcp.linxule.workers.dev/mcp. No API key needed.

For clients supporting Streamable HTTP, connect directly to the URL. For stdio-only clients, use mcp-remote:

{
  "mcpServers": {
    "lotus-wisdom": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://lotus-wisdom-mcp.linxule.workers.dev/mcp"]
    }
  }
}

To self-host your own instance, see [worker/README.md](worker/README.md).

Building from source

bun install
bun run build
bun run start

Enable debug mode:

LOTUS_DEBUG=true bun run start

Interactive Visualization (ext-apps)

In MCP clients that support ext-apps (Claude Desktop, Cursor, ChatGPT), the tool renders an interactive "Living Trace" visualization inline in the chat:

• Journey trace: SVG circles colored by wisdom domain appear as steps arrive
• Domain colors: Process (gold), Skillful Means (amber), Non-Dual (green), Meta-Cognitive (blue), Meditation (teal)
• Meditation breathing: Hollow circles with gentle inhale/exhale animation
• Completion: Journey resolves into a gradient path showing the full domain arc
• Click to explore: Pin any step to read its contemplation text
• Collapse for long journeys: Shows last 8 steps with a "+N" cluster for earlier ones

Clients without ext-apps support are unaffected — they receive the same JSON tool responses as before.

How It Works

The Lotus Wisdom framework recognizes that wisdom often emerges not through linear thinking but through a dance between different modes of understanding. The tool facilitates this by:

1. Tracking Wisdom Domains: As you move through different tags, the tool tracks which wisdom domains you're engaging, helping you see the shape of your inquiry.

1. Journey Consciousness: The tool maintains awareness of your complete journey, showing both the sequence of tags used and the movement between wisdom domains.

1. Non-Linear Progress: While steps are numbered, the process isn't strictly linear. You can revisit, revise, and branch as understanding deepens.

1. Integration Points: Tags like integrate, transcend, and embody help weave insights together rather than keeping them separate.

1. Natural Expression: The tool handles the contemplative process, but the final wisdom is always expressed naturally by the AI, not as formatted output.

Token Optimization Design

MCP tool descriptions stay in the AI's context window constantly when the server is connected. To minimize this overhead while preserving the full teaching content:

• Constant context (~150 tokens): The lotuswisdom tool description is kept minimal—just enough for the AI to know when and how to use it
• On-demand learning (~1,200 tokens): The complete framework is delivered when calling with tag='begin', including:
• Philosophy and domain spirits
• Parameter explanations (tag, content, stepNumber, etc.)
• Response format details (wisdomDomain, journey, domainJourney)
• Meditation handling (MEDITATION_COMPLETE status)
• When to use guidance
• Learn first, practice second: The begin tag ensures models receive complete understanding before contemplating

This approach reduces constant context overhead by ~85% when the tool is idle. When actually used, the full framework is delivered on first step—nothing is lost.

License

This MCP server is licensed under the MIT License. For more details, ple

…

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: linxule
• Source: linxule/lotus-wisdom-mcp
• License: MIT

Install and usage instructions live in the source repository linked above.