Lotus Wisdom
Server Details
Contemplative reasoning with Lotus Sutra wisdom framework and ext-apps visualization.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- linxule/lotus-wisdom-mcp
- GitHub Stars
- 31
- Server Listing
- Lotus Wisdom MCP Server
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.8/5 across 2 of 2 tools scored. Lowest: 3.2/5.
The two tools have distinctly different purposes: one is the main contemplative reasoning tool, the other provides a summary of the journey. No overlap in functionality.
Both tools share the 'lotuswisdom' prefix, making them predictable. However, the pattern is noun_noun rather than verb_noun, and the main tool's name is identical to the server name, which slightly reduces clarity.
With only two tools, the set feels thin for a reasoning server. The main tool is complex and handles most functionality, but a few more focused tools (e.g., reset, list sessions) might improve scope.
The core workflow (begin, contemplate, receive wisdom, get summary) is covered. Missing explicit tools for resetting or managing multiple journeys, but these are minor gaps given the main tool's flexibility.
Available Tools
2 toolslotuswisdomLotus WisdomARead-onlyIdempotentInspect
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 (wisdom-domain tag) | |
| content | Yes | Your contemplation for this step | |
| stepNumber | No | Current step number | |
| totalSteps | No | Estimated total steps needed (adjustable as you go) | |
| isMeditation | No | Whether this step is a meditative pause | |
| nextStepNeeded | No | Whether another step is needed | |
| previousJourney | No | Pass the journey string from the previous response to maintain journey tracking (e.g. "begin → open → examine"). | |
| meditationDuration | No | Duration for the meditation pause in seconds (1-10) |
Output Schema
| Name | Required | Description |
|---|---|---|
| steps | No | |
| prompt | No | |
| status | Yes | |
| journey | No | |
| welcome | No | |
| duration | No | |
| finalStep | No | |
| stepNumber | No | |
| totalSteps | No | |
| currentStep | No | |
| instruction | No | |
| finalJourney | No | |
| wisdomDomain | No | |
| contemplation | No | |
| domainJourney | No | |
| journeyLength | No | |
| processLength | No | |
| nextStepNeeded | No | |
| processComplete | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds workflow constraints (tag sequence, status condition) and behavioral rules (e.g., 'Do NOT output wisdom until status='WISDOM_READY''), which go beyond annotations without contradicting them.
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 front-loaded with the purpose and workflow, then lists tags efficiently. It uses a clear structure with sections for workflow and tag categories. Every sentence adds value, though it could be slightly more concise by removing redundant examples.
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 complexity (8 parameters, enums, output schema), the description covers all essential aspects: purpose, workflow, tag ordering, and the status condition. The presence of an output schema means return values do not need to be detailed in the description.
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 100% description coverage, listing all parameters with descriptions. The description adds value by grouping tags into categories (process, meta-cognitive, non-dual, skillful-means) and explaining the workflow order (e.g., 'begin' must come first).
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 defines it as a 'Contemplative reasoning tool' with specific use cases: 'complex problems needing multi-perspective understanding, contradictions requiring integration, or questions holding their own wisdom.' It distinguishes from the sibling tool 'lotuswisdom_summary' by focusing on reasoning rather than summarization.
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?
Explicit instructions are provided: 'Always start with tag='begin'... Then continue with contemplation tags. Do NOT output wisdom until status='WISDOM_READY'.' The description states when to use it (complex problems) but does not explicitly state when not to use it, though the context implies simpler problems are not suitable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
lotuswisdom_summaryLotus Wisdom Journey SummaryBRead-onlyIdempotentInspect
Get a summary of the current contemplative journey
| Name | Required | Description | Default |
|---|---|---|---|
| previousJourney | No | Pass the journey string from a previous response to reconstruct the current journey summary. |
Output Schema
| Name | Required | Description |
|---|---|---|
| steps | Yes | |
| status | Yes | |
| domainJourney | Yes | |
| journeyLength | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is clear. The description adds minimal context by stating it returns a summary, but does not elaborate on what 'current' means or how the optional parameter affects behavior. No contradiction with annotations.
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, concise sentence that gets straight to the point. It is front-loaded and contains no unnecessary words, though it could potentially include more detail without harming conciseness.
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's simplicity (one optional parameter, output schema present, annotations clear), the description is adequate but lacks explanation of key terms like 'current journey' or how to use 'previousJourney' effectively. The presence of an output schema partially compensates for missing return value details.
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 coverage is 100%, so the schema already documents the single parameter 'previousJourney'. The description does not add any additional meaning or constraints beyond what is in the schema, meeting the baseline of 3.
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 uses a specific verb 'Get' and resource 'summary of the current contemplative journey', clearly indicating the tool's purpose. However, it does not distinguish itself from the sibling tool 'lotuswisdom', which might also involve summaries.
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 no guidance on when to use this tool versus the sibling tool 'lotuswisdom'. There is no mention of prerequisites, when not to use it, or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Servers
- Flicense-qualityDmaintenanceEnables philosophical reasoning and concept analysis through NARS non-axiomatic logic integration, supporting multi-perspective synthesis, epistemic uncertainty tracking, and contextual semantic exploration with built-in truth maintenance.8
- Alicense-qualityCmaintenanceA cognitive scripting language for structured reasoning with LLMs. 12 built-in operations (survey, ground, assert, doubt, contrast, synthesize, etc.) for guiding AI through dialectical analysis, impact assessment, and other thinking patterns.Apache 2.0
- AlicenseAqualityDmaintenanceStructured reasoning MCP server that decomposes problems into atomic steps (premise, reasoning, hypothesis, verification, conclusion) with confidence scoring, live visualization, and approval feedback.373MIT
- FlicenseCqualityDmaintenanceProvides Universal Ethical Framework (UEF) and Recursive Doubt Engine tools for analyzing decisions, assessing alignment, and generating insights for consciousness evolution.7
Your Connectors
Sign in to create a connector for this server.