Lotus Wisdom MCP Server
Lotus Wisdom MCP Server enables structured contemplative problem-solving through a step-by-step wisdom workflow, journey summaries, and guided prompts.
Run a
lotuswisdominquiry, starting withtag='begin'to receive the full framework, then progressing through process, meta-cognitive, non-dual, skillful-means, and meditation tags.Track the tag journey and wisdom-domain movements across steps, including continuity in stateless clients via
previousJourney.Add meditative pauses with optional duration, and complete the inquiry only when the tool returns
WISDOM_READY.Summarize an existing contemplative journey with
lotuswisdom_summary.Use the
contemplateanddeep-inquiryprompts for guided contemplative sessions.Consume text plus structured output, and in supporting MCP ext-apps clients see each step as an interactive halo visualization.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Lotus Wisdom MCP Serverhelp me decide whether to accept the job offer or start my own business"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
🪷 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) — a pixel halo that lights one segment per step, in night and day variants that follow the host theme; keyboard-accessible and respects reduced motion
MCP Prompts (
contemplate,deep-inquiry) for one-step guided contemplative sessionsStructured tool output (
structuredContent+outputSchema) alongside the text responseTracks both tag journey and wisdom domain movements
Available as a local stdio package (
npx) or a hosted remote ConnectorFinal integration of insights into a clear response
Related MCP server: Enhanced Sequential Thinking MCP Server
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.
The MCP server exposes the framework through tools and prompts. How well a model follows that framework depends on the model and host.
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 (🚪):
beginBegin your journey here - receives the full framework before contemplation starts
Skillful Means (🔆):
upaya,expedient,direct,gradual,suddenDifferent approaches to truth - sometimes direct pointing, sometimes gradual unfolding
Non-Dual Recognition (☯️):
recognize,transform,integrate,transcend,embodyAspects of awakening to what's already present - recognition IS transformation
Meta-Cognitive (🧠):
examine,reflect,verify,refine,completeThe mind watching its own understanding unfold
Process Flow (🌊):
open,engage,expressA natural arc that can contain any of the above approaches
Meditation (🧘):
meditatePausing to let insights emerge from stillness
Thought Visualization
In clients that support MCP ext-apps, each step renders inline as an interactive halo (see Interactive Visualization 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
The user submits a problem to solve
The model begins with
tag='begin'to receive the full frameworkThe model continues with contemplation tags (open, examine, integrate, etc.)
Each thought builds on previous ones and may revise understanding
The tool tracks both the tag journey and wisdom domain movements
Meditation pauses can be included for clarity
When status='WISDOM_READY' is returned, the tool's work is complete
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(non-empty string, required): The content of the current processing step, includingbeginstepNumber(integer, optional, default1): Current number in sequencetotalSteps(integer, optional, default5): Estimated total steps needednextStepNeeded(boolean, optional, defaulttrue): Whether another step is neededisMeditation(boolean, optional): Whether this step is a meditative pausemeditationDuration(integer, optional): Duration for meditation in seconds (1-10)previousJourney(string, optional): Thejourneystring 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: a JSON text block and structuredContent validated against the tool's
outputSchema. Every successful response includes journey for continuity,
including begin and completion. Completion also retains finalJourney for
compatibility. For begin, the full framework is in the text block; structured
output contains its status, welcome, contemplation, journey, domain journey,
and step counts. Other result variants retain their fields in both representations.
A meditation step with nextStepNeeded: false completes the inquiry with
WISDOM_READY; ongoing meditation steps return MEDITATION_COMPLETE.
Response statuses include:
Processing status with current step information, wisdom domain, and journey tracking
FRAMEWORK_RECEIVEDstatus on abeginstepMEDITATION_COMPLETEstatus for meditation stepsWISDOM_READYstatus when the contemplative process is complete
The tool declares readOnlyHint, idempotentHint, destructiveHint: false, and openWorldHint: false. These are host hints, not a guarantee of zero side effects: local stdio calls update the in-memory journey, and the hosted worker records usage analytics. The tools do not modify user files or external business data.
lotuswisdom_summary
Get a summary of the current contemplative journey.
Inputs:
previousJourney(string, optional): Thejourneystring 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— argumentquestion: opens a single-question contemplation, instructing the model to start withtag='begin', iterate, and speak the wisdom only oncestatus='WISDOM_READY'.deep-inquiry— argumenttopic: 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:
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
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
}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
}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
}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
}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
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-mcpClaude Desktop
Add to your claude_desktop_config.json:
OS | Config path |
macOS |
|
Windows |
|
Linux |
|
{
"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-mcpChatGPT
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.
Building from source
Use Bun 1.4.2 and Node.js 24 to match CI.
bun install --frozen-lockfile
bun run typecheck
bun run test
bun run build
bun run test:package
bun run startThe build installs the app's locked dependencies and rebuilds the tracked
dist/bundle.js and dist/journey.html artifacts. Check the app with
cd app && bunx tsc --noEmit && bun run test. The suite covers the single-file
build (the local Vite plugin inlines JavaScript and CSS into one HTML resource;
tests cover dynamic imports and HTML escaping) and the journey itself:
continuity across frames, meditation, reset between inquiries, exploration of
the completed panel only, and every tag's character rendering from embedded
outlines. See worker validation for the worker
typecheck, dry-run build, and local HTTP regression.
To see a journey the way a chat shows it, one frame per call, run
cd app && bunx vite build and open app/preview.html. The halo's characters
are generated into app/src/glyphs.ts; app/scripts/glyphs.py regenerates
them from a Noto Serif TC subset and documents how to fetch it.
bun run test:package packs the built files, installs the tarball in a temporary
consumer directory, and starts npm's executable link. It checks the MCP version,
tool discovery, a complete inquiry ending in meditation, a fresh inquiry reset,
and the packaged journey HTML. CI runs this check
after building and before publishing, so a broken launcher or missing package
asset blocks the release. It requires npm registry access to install production
dependencies and removes its temporary files on completion.
Dependabot uses the bun ecosystem for the root, app, and worker packages so
updates include their bun.lock files. CI verifies all three packages on pull
requests. Merging a PR updates source only: a version tag publishes to npm and
the MCP Registry, and the Cloudflare Worker requires a separate deployment.
If npm publishes successfully but MCP Registry registration fails, retry only registration for the existing tag:
gh workflow run publish-mcp.yml --ref main -f registry_tag=vX.Y.ZReplace vX.Y.Z with the existing published version's tag. This revalidates the
tagged source and waits for npm availability before
registration. It does not republish npm or move the release tag.
Enable debug mode:
LOTUS_DEBUG=true bun run startInteractive Visualization (ext-apps)
In MCP clients that support ext-apps (Claude Desktop, Cursor, ChatGPT), each step renders inline as a small panel: a ring of pixels modeled on the LED halos (光背) wired behind temple statues, the step's character at its center, and the contemplation beside it. Each tool call gets its own panel, so a journey reads down the conversation one frame at a time.
The ring: one segment per step, shaded with the ASCII ramp: a ▒ track for steps still estimated to come, ▓ for earlier steps, solid █ for the current one, which dithers in as it arrives. The ring re-divides as
totalStepschanges and closes exactly when the journey completes.Color is attention: only the current step is in color; the rest is ink. The colors are the six of the Buddha's aura from the Buddhist flag: Entry white, Process red, Skillful Means yellow, Non-Dual orange, Meta-Cognitive blue, and Meditation the flag's sixth band, a pixel mosaic of all five. When the circle closes it turns white, every color at once (ink in daylight).
A character for each tag: every character comes from Kumārajīva's Lotus Sutra or from Tiantai's reading of it, in traditional forms. open, express, recognize and embody spell 開示悟入, the four aims of the Buddha's appearance in chapter 2, the chapter on skillful means, which also gives 巧 (upaya: 巧說諸法, "skillfully expounds the teachings") and 直 (direct: 正直捨方便, "directly setting means aside"). 照 (reflect) is the light across eighteen thousand worlds (ch. 1), 出 (transcend) the children leaving the burning house (ch. 3), 證 (verify) Many Treasures bearing witness (ch. 11), 淨 (refine) the purified senses (ch. 19). examine and meditate make Tiantai's 止觀. Hover a character for its pinyin and source. Characters are drawn from embedded Noto Serif TC outlines (SIL OFL 1.1), so they look the same with or without CJK fonts.
Meditation: the meditation segment breathes by pixel density three times, then rests.
Completion: the circle closes and turns white, segment by segment. Nothing is added underneath.
Look back: only the completed panel is interactive. Hover or focus a segment to see that step in its color with its character and tag; click or press Enter to pin it; arrow keys walk the ring. Panels for steps still in progress are for reading and do not respond to the pointer. Steps carried over from earlier frames show their place; their words stay in those frames.
Quiet by design: nothing loops, nothing glows, and with
prefers-reduced-motionevery state appears without animation. The design came out of a Lotus Wisdom journey on its own UI; aftercd app && bunx vite build, openapp/preview.htmlto replay that journey as a chat shows it, one frame per call.
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:
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.
Journey Consciousness: The tool maintains awareness of your complete journey, showing both the sequence of tags used and the movement between wisdom domains.
Non-Linear Progress: While steps are numbered, the process isn't strictly linear. You can revisit, revise, and branch as understanding deepens.
Integration Points: Tags like
integrate,transcend, andembodyhelp weave insights together rather than keeping them separate.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
lotuswisdomtool description is kept minimal—just enough for the AI to know when and how to use itOn-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
begintag 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, please see the LICENSE file in the project repository.
Contributing
Contributions are welcome! Please feel free to submit issues or pull requests on the GitHub repository.
Version
Current version: 0.9.1
What's New in 0.9.1
Updated the MCP SDK, MCP Apps, Cloudflare Agents, and Worker tooling from the grouped Dependabot updates.
Refreshed transitive
@hono/node-serverandqsdependencies to patched versions, clearing the dependency audit findings in the server and worker.Retained the SDK v1 compatibility handler and aligned the worker's MCP dependencies with Cloudflare Agents' required peer versions.
Removed the temporary Undici override now that Worker tooling requires the patched version directly.
What's New in 0.9.0
Redesigned the journey visualization as a pixel halo: one segment per step, a traditional character at its center, and the contemplation beside it. Earlier steps appear in ink, future steps as a dim track, and the current step in its domain color. The completed circle turns white in the night theme.
Added embedded Noto Serif TC glyph outlines, with pinyin and source notes on hover, so the characters render without installed CJK fonts or font requests.
Gave each event a finite animation and made only the completed panel explorable by hover, focus, pin, and keyboard navigation.
Kept completed panels at a steady height while looking back, with internal scrolling for longer earlier text, and fixed a rapid-hover content fade race.
Added a daylight palette that follows the host theme, reduced-motion styles, DOM regressions, and a development preview showing one frame per tool call.
Includes the journey reliability and dependency security fixes below; 0.8.6 was prepared but not published separately.
Reliability fixes prepared for 0.8.6 (included in 0.9.0)
Preserved the opening step in stateless journeys by returning continuity metadata from
begin, including in structured output.Fixed completion on meditation steps and added
journeyto completion responses while retainingfinalJourney.Reset the visualization between inquiries in reused iframes, including pending animations, collapsed steps, pins, and completion effects.
Added response-driven journey regressions for the installed CLI and worker, plus DOM tests for consecutive inquiries and animation races.
Updated dependencies and patched vulnerable transitive packages. The worker temporarily overrides Undici until Wrangler's Miniflare dependency is patched.
What's New in 0.8.1
Updated dependencies and GitHub Actions, regenerated Bun lockfiles, and added app and worker validation to CI.
Updated vulnerable transitive dependencies across all three packages and added dependency audits to CI. All three Bun audits passed during release validation on September 14, 2026.
Migrated the visualization to ext-apps 2 with its MCP client v2 and Zod 4 dependencies. The server transports remain on MCP SDK v1; the worker uses the agents SDK's explicit compatibility handler and retains stateless JSON responses.
What's New in 0.8.0
Single source of truth: domain logic, tool/server metadata, prompts, and the client parser now live in
src/shared/and are imported by both the stdio entry (index.ts) and the Cloudflare Worker — no more local-vs-remote driftHigh-level
McpServereverywhere: the local stdio server was migrated from the low-levelServerAPI toMcpServer, matching the WorkerMCP Prompts:
contemplateanddeep-inquiryfor guided contemplative sessionsStructured tool output: tools now return
structuredContentvalidated against anoutputSchema, plus behavioral annotations (readOnlyHint,idempotentHint,destructiveHint: false,openWorldHint: false) and a serverinstructionsfieldTheme-aware, accessible UI: the ext-apps journey visualization adapts to the host light/dark theme and is keyboard-accessible
Security & cleanup:
@modelcontextprotocol/sdkbumped to^1.27.1,zodadded,chalkremoved; the legacy Express SSE server (server.ts), theexpressdeps, and theDockerfilewere removed; the repo moved to bun lockfiles. Added a vitest test suite (tests/) and a single-source version workflow (src/shared/version.ts+bun run sync-version)Server icon & website:
server.jsonadvertises the remote Worker (remotes[]), awebsiteUrl, and iconsizes; the Worker advertises icons/website in the initialize handshake and serves the icon as same-origin bytes at/icon.png
What's New in 0.7.0
Fully stateless Worker: removed the Durable Object — the remote Worker now creates a fresh server per request and relies on the client-driven
previousJourneyparameter for journey continuity (eliminating accumulated SSE wall time)
What's New in 0.6.0
Server icon: added an icon to
server.jsonand the Worker so MCP Registry and claude.ai Connectors display the lotus logoWarmer UI and (since reverted) experimental Durable Object session state
What's New in 0.5.0
npm + MCP Registry publish: hardened packaging and published to npm and the official MCP Registry
What's New in 0.4.0
Interactive Visualization: MCP ext-apps UI renders a "Living Trace" journey inline in supporting clients (Claude Desktop, Cursor, ChatGPT)
Completion Fix: Any tag with
nextStepNeeded=falsenow correctly returnsWISDOM_READY(previously onlyexpressandcompletecould complete)Cloudflare Worker: Worker deployment updated with ext-apps resource serving
What's New in 0.3.2
🚪 Simplified Begin:
tag='begin'can now be called with just{"tag":"begin"}- all other params auto-filled🤖 Better Haiku/Small Model Support: Removes friction for models that don't infer all required params
What's New in 0.3.1
📚 Complete Framework Learning:
begintag now returns full parameter explanations, response format details, and meditation handling🔢 Accurate Token Counts: Updated documentation with actual token measurements (~150 constant, ~1,200 on-demand)
What's New in 0.3.0
🚪 Begin Tag: New
tag='begin'opens the journey—returns full framework before contemplation starts⚡ Optimized Token Footprint: Reduced constant context overhead from ~1400 to ~200 tokens while preserving full teaching content
🧘 Learn First, Practice Second: The
begintag ensures models receive complete understanding before contemplating📦 Updated SDK: Upgraded to @modelcontextprotocol/sdk 1.23.0
What's New in 0.2.1
📋 MCP Registry Enhancement: Added
titlefield for better discoverability🎯 Full Compliance: Now fully compliant with official MCP publishing guide
🔗 Registry Links: Available on Official MCP Registry
What's New in 0.2.0
🌐 HTTP Transport Support: Now deployable on smithery.ai and other HTTP-based platforms
🔄 Dual Transport: Maintains stdio support for npm/CLI users while adding HTTP for remote deployment
📦 Updated SDK: Upgraded to @modelcontextprotocol/sdk 1.20.1 with Streamable HTTP support
🪷 New Logo: Terminal-aesthetic lotus logo perfect for developer tools
⚡ Session Management: HTTP version includes full session management for stateful wisdom journeys
Available Tools
2 toolslotuswisdomAInspect
Contemplative reasoning tool. Use for complex problems needing multi-perspective understanding, contradictions requiring integration, or questions holding their own wisdom.
Workflow: Always start with tag='begin' (returns framework). Then continue with contemplation tags. Do NOT output wisdom until status='WISDOM_READY'.
Tags: begin (FIRST - receives framework), then: open/engage/express (process), examine/reflect/verify/refine/complete (meta-cognitive), recognize/transform/integrate/transcend/embody (non-dual), upaya/expedient/direct/gradual/sudden (skillful-means), meditate (pause).
| Name | Required | Description | Default |
|---|---|---|---|
| tag | Yes | Current processing technique | |
| content | Yes | Content of the current processing step | |
| stepNumber | Yes | Current step number | |
| totalSteps | Yes | Estimated total steps needed | |
| nextStepNeeded | Yes | Whether another step is needed | |
| isMeditation | No | Whether this step is a meditative pause | |
| meditationDuration | No | Duration for meditation in seconds |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: the sequential workflow requirement, the prohibition against premature wisdom output, and the categorization of tags into functional roles. It doesn't mention error handling, rate limits, or authentication needs, but provides substantial operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded with purpose and usage guidelines. The tag categorization is necessary but somewhat dense. Every sentence earns its place by providing essential operational information, though the tag list could be more efficiently presented.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex 7-parameter tool with no annotations and no output schema, the description provides good contextual completeness. It explains the multi-step workflow, tag semantics, and operational constraints. The main gap is lack of information about return values or output format, but given the tool's contemplative nature, the description covers most essential usage context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 7 parameters thoroughly. The description adds value by explaining the purpose of the 'tag' parameter through categorization and workflow rules, but doesn't provide additional meaning for other parameters like 'content' or 'stepNumber' beyond what the schema offers. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool is for 'contemplative reasoning' and specifies use cases like 'complex problems needing multi-perspective understanding' and 'contradictions requiring integration'. It distinguishes from the sibling 'lotuswisdom_summary' by focusing on the reasoning process rather than summarization. However, it doesn't explicitly contrast with the sibling tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit workflow guidance: 'Always start with tag='begin'' and 'Do NOT output wisdom until status='WISDOM_READY''. It also categorizes tags into functional groups (process, meta-cognitive, non-dual, skillful-means, pause), giving clear context for when to use different tags. No explicit alternatives are mentioned, but the structured guidance is comprehensive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lotuswisdom_summaryBInspect
Get a summary of the current contemplative journey
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool 'Get[s] a summary,' which implies a read-only operation, but doesn't clarify aspects like authentication needs, rate limits, or what the summary contains (e.g., format, depth). This leaves significant gaps for a tool with no structured safety hints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with zero wasted words. It is appropriately sized and front-loaded, directly stating the tool's purpose without unnecessary elaboration, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 0 parameters, no annotations, and no output schema, the description is minimally complete. It states what the tool does but lacks details on behavioral traits, output format, or differentiation from siblings. For a simple tool with no complexity, this is adequate but leaves clear gaps in usage context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description doesn't mention any parameters, which is appropriate here. Since there are no parameters to explain, it meets the baseline of 4, as it doesn't need to compensate for missing schema information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get a summary') and the resource ('current contemplative journey'), providing a specific verb+resource combination. However, it doesn't differentiate from its sibling tool 'lotuswisdom' (which presumably handles the journey itself rather than summarizing it), so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no guidance on when to use this tool versus its sibling 'lotuswisdom' or any alternatives. It implies usage in the context of a 'contemplative journey' but doesn't specify prerequisites, timing, or exclusions, leaving the agent with minimal contextual direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
- First observed
lotuswisdom - First observed
lotuswisdom_summary
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: 'lotuswisdom' is for active contemplative reasoning with a structured workflow, while 'lotuswisdom_summary' is for retrieving a summary of the current journey. There is no overlap or ambiguity between them.
Both tools follow a consistent naming pattern with the 'lotuswisdom' prefix and descriptive suffixes ('lotuswisdom' and 'lotuswisdom_summary'). This creates a predictable and readable convention across the tool set.
With only two tools, the server feels under-scoped for its apparent domain of contemplative reasoning and wisdom generation. The core 'lotuswisdom' tool has a complex workflow with many tags, suggesting that additional supporting tools (e.g., for managing or querying specific aspects) would be appropriate to provide a more complete surface.
The tool set is severely incomplete for the domain. While 'lotuswisdom' handles the main reasoning process and 'lotuswisdom_summary' provides summaries, there are obvious gaps: no tools for managing or resetting contemplative sessions, querying intermediate states, or handling errors within the complex workflow. This will likely cause agent failures or dead ends.
Maintenance
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