Skip to main content
Glama

HUMMBL MCP Server

Model Context Protocol server providing access to the HUMMBL Base120 mental models framework.

CI npm version License: MIT

Overview

HUMMBL Base120 is a comprehensive cognitive framework consisting of 120 validated mental models organized across 6 transformations:

  • P (Perspective): Change viewpoint to see problems differently

  • IN (Inversion): Flip problem to find solution by avoiding failure

  • CO (Composition): Combine elements to create emergent properties

  • DE (Decomposition): Break down complexity into manageable components

  • RE (Recursion): Apply patterns at multiple scales and iterations

  • SY (Meta-Systems): Understand rules, patterns, and systems governing systems

Installation

npm install -g @hummbl/mcp-server

Using npx (No Installation Required)

npx @hummbl/mcp-server

Configuration

Claude Desktop

Add to your Claude Desktop configuration file:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

  • Windows: %APPDATA%\Claude\claude_desktop_config.json

{ "mcpServers": { "hummbl": { "command": "npx", "args": ["-y", "@hummbl/mcp-server"] } } }

get_methodology

Retrieve the canonical Self-Dialectical AI Systems methodology, including all stages and HUMMBL Base120 references.

Example:

{}

audit_model_references

Audit a list of HUMMBL model references for validity, duplication, and transformation alignment.

Example:

{ "items": [ { "code": "IN11", "expectedTransformation": "IN" }, { "code": "CO4" } ] }

After configuration, restart Claude Desktop. The HUMMBL tools will appear in the attachment menu.

Available Tools

get_model

Retrieve detailed information about a specific mental model.

Example:

{ "code": "P1" }

list_all_models

List all 120 mental models, optionally filtered by transformation type.

Example:

{ "transformation_filter": "P" }

search_models

Search models by keyword across names, descriptions, and examples.

Example:

{ "query": "decision" }

recommend_models

Get AI-recommended models based on problem description.

Example:

{ "problem_description": "Our startup is growing rapidly but systems are breaking down. We need to scale operations without losing quality." }

get_transformation

Retrieve information about a specific transformation type and all its models.

Example:

{ "type": "IN" }

search_problem_patterns

Find pre-defined problem patterns with recommended approaches.

Example:

{ "query": "innovation" }

Usage Examples

Example 1: Getting a Specific Model

Scenario: You want to understand "First Principles Thinking" before applying it to a problem.

// Request { "tool": "get_model", "arguments": { "code": "P1" } } // Response { "model": { "code": "P1", "name": "First Principles Framing", "definition": "Reduce complex problems to foundational truths that cannot be further simplified", "priority": 1, "transformation": "P" } }

When to use: Starting a new problem analysis by identifying core assumptions and fundamentals.


Example 2: Listing Models by Transformation

Scenario: You know you need to look at a problem from different perspectives but want to see all available perspective models.

// Request { "tool": "list_all_models", "arguments": { "transformation_filter": "P" } } // Response { "total": 20, "models": [ { "code": "P1", "name": "First Principles Framing", "definition": "Reduce complex problems to foundational truths...", "priority": 1, "transformation": "P" }, { "code": "P2", "name": "Stakeholder Mapping", "definition": "Identify all parties with interest, influence...", "priority": 1, "transformation": "P" } // ... 18 more models ] }

When to use: Exploring all models within a specific transformation category to find the right approach.


Scenario: You're making a strategic decision and want to find all mental models related to decision-making.

// Request { "tool": "search_models", "arguments": { "query": "decision" } } // Response { "query": "decision", "resultCount": 8, "results": [ { "code": "P2", "name": "Stakeholder Mapping", "definition": "Identify all parties with interest, influence, or impact in a system or decision", "priority": 1, "transformation": "P" }, { "code": "SY3", "name": "Decision Trees & Game Theory", "definition": "Model sequential choices and strategic interactions with payoff structures", "priority": 1, "transformation": "SY" } // ... 6 more results ] }

When to use: Finding relevant models across all transformations for a specific concept or challenge.


Example 4: Getting Recommendations for a Complex Problem

Scenario: Your startup is scaling rapidly but systems are breaking down—you need guidance on which mental models to apply.

// Request { "tool": "recommend_models", "arguments": { "problem": "Our startup is growing rapidly but systems are breaking down. We need to scale operations without losing quality." } } // Response { "problem": "Our startup is growing rapidly but systems are breaking down...", "recommendationCount": 2, "recommendations": [ { "pattern": "Complex system to understand", "transformations": [ { "key": "DE", "name": "Decomposition", "description": "Break down complexity into manageable components" } ], "topModels": [ { "code": "DE1", "name": "Modular Decomposition", "definition": "Break systems into independent, interchangeable components...", "priority": 1 }, { "code": "DE2", "name": "Layered Architecture", "definition": "Organize systems into hierarchical strata with clear interfaces", "priority": 1 } ] }, { "pattern": "Strategic or coordination challenge", "transformations": [ { "key": "SY", "name": "Meta-Systems", "description": "Understand rules, patterns, and systems governing systems" } ], "topModels": [ { "code": "SY1", "name": "Feedback Loops & Causality", "definition": "Trace how outputs loop back as inputs creating reinforcing or balancing dynamics", "priority": 1 } ] } ] }

When to use: You have a complex, multi-faceted problem and need AI-driven recommendations on where to start.


Example 5: Exploring the Inversion Transformation

Scenario: You've heard about "inversion thinking" and want to understand all the models in that category.

// Request { "tool": "get_transformation", "arguments": { "key": "IN" } } // Response { "key": "IN", "name": "Inversion", "description": "Reverse assumptions. Examine opposites, edges, negations.", "modelCount": 20, "models": [ { "code": "IN1", "name": "Subtractive Thinking", "definition": "Improve systems by removing elements rather than adding complexity", "priority": 1 }, { "code": "IN2", "name": "Premortem Analysis", "definition": "Assume failure has occurred and work backward to identify causes", "priority": 1 } // ... 18 more models ] }

When to use: Deep-diving into a transformation to understand its philosophy and available models.


Example 6: Finding Problem Patterns

Scenario: Your team struggles with innovation—everything feels incremental. You want to find pre-defined patterns that match this challenge.

// Request { "tool": "search_problem_patterns", "arguments": { "query": "innovation" } } // Response { "query": "innovation", "patternCount": 1, "patterns": [ { "pattern": "Stuck in conventional thinking", "transformations": ["IN"], "topModels": ["IN1", "IN2", "IN3"] } ] }

When to use: You recognize a common problem type and want to quickly jump to the recommended mental models and approaches.


Guided Workflows (NEW in Phase 2)

HUMMBL now includes guided multi-turn workflows that walk you through systematic problem-solving using mental models. Perfect for complex problems that benefit from structured analysis.

Available Workflows

1. Root Cause Analysis

Use when: Investigating failures, incidents, or recurring problems Duration: 20-30 minutes Sequence: P → IN → DE → SY

Systematically find root causes, not just symptoms.

2. Strategy Design

Use when: Creating strategies, planning initiatives, entering markets Duration: 30-45 minutes Sequence: P → CO → SY → RE

Design comprehensive strategies with creative combinations and systemic thinking.

3. Decision Making

Use when: High-stakes decisions with uncertainty Duration: 15-25 minutes Sequence: P → IN → SY → RE

Make quality decisions through clear framing, stress-testing, and systematic evaluation.

Workflow Tools

list_workflows

List all available guided workflows.

{ "tool": "list_workflows" }

start_workflow

Begin a guided workflow for your problem.

{ "tool": "start_workflow", "arguments": { "workflow_name": "root_cause_analysis", "problem_description": "Our production API started failing intermittently after yesterday's deployment" } }

continue_workflow

Proceed to the next step after completing current step.

{ "tool": "continue_workflow", "arguments": { "workflow_name": "root_cause_analysis", "current_step": 1, "step_insights": "Identified 3 affected stakeholders: customers experiencing timeouts, internal services with cascading failures, and ops team receiving alerts. Core assumption: the deployment changed something fundamental in request handling." } }

find_workflow_for_problem

Discover which workflow best fits your problem.

{ "tool": "find_workflow_for_problem", "arguments": { "problem_keywords": "system failure production" } }

Example: Root Cause Analysis Workflow

Step 1 (Perspective):

{ "currentStep": 1, "totalSteps": 4, "transformation": "P", "guidance": "Frame the problem clearly from multiple perspectives", "suggestedModels": ["P1", "P2", "P15"], "questions": [ "What are the foundational facts we know for certain?", "Who is affected and how?", "What assumptions are we making?" ] }

After completing Step 1, continue:

{ "tool": "continue_workflow", "arguments": { "workflow_name": "root_cause_analysis", "current_step": 1, "step_insights": "Your insights here..." } }

Step 2 (Inversion): Test boundaries, work backward from failure Step 3 (Decomposition): Isolate the failing component Step 4 (Meta-Systems): Design systemic fixes and prevention


Available Resources

Direct URI-based access to models and transformations:

  • hummbl://model/{code} – Individual model (e.g., hummbl://model/P1)

  • hummbl://transformation/{type} – All models in transformation (e.g., hummbl://transformation/P)

  • hummbl://models – Complete Base120 framework

  • hummbl://methodology/self-dialectical-ai – Structured Self-Dialectical AI methodology definition

  • hummbl://methodology/self-dialectical-ai/overview – Markdown overview of the methodology for quick operator reference

Self-Dialectical Methodology Overview

The HUMMBL Self-Dialectical AI Systems methodology (v1.2) enables ethical self-correction via five dialectical stages (thesis, antithesis, synthesis, convergence, meta-reflection) mapped to Base120 mental models plus SY meta-models. Use the tools/resources above to fetch the canonical JSON definition, Markdown overview, or to audit references in external documents.

Problem Patterns

HUMMBL includes pre-defined problem patterns that map common challenges to recommended transformations and models. See Problem Patterns Documentation for the complete catalog with detailed guidance.

Problem Patterns

HUMMBL includes pre-defined problem patterns that map common challenges to recommended transformations and models. See Problem Patterns Documentation for the complete catalog with detailed guidance.

Development

Setup

git clone https://github.com/hummbl-dev/mcp-server.git cd mcp-server npm install

Build

npm run build

Run Locally

npm run dev

Type Checking

npm run typecheck

Architecture

src/ ├── index.ts # stdio entry point ├── server.ts # Server configuration ├── framework/ │ └── base120.ts # Complete mental models database ├── tools/ │ └── models.ts # Tool registrations ├── resources/ │ └── models.ts # Resource endpoints ├── types/ │ └── domain.ts # Core type definitions └── utils/ └── result.ts # Result pattern utilities

License

MIT © HUMMBL, LLC

Version

1.0.0-beta.1

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/hummbl-dev/mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server