Give Claude access to 120 validated mental models — for sharper analysis, clearer decision-making, and systematic problem-solving across six cognitive transformations.
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
npm install -g @hummbl/mcp-servernpx @hummbl/mcp-serverThe same build is mirrored to GitHub Packages as @hummbl-dev/mcp-server. GitHub Packages requires a GitHub personal access token with read:packages scope even for public installs — create one at https://github.com/settings/tokens.
Add to your project's .npmrc:
@hummbl-dev:registry=https://npm.pkg.github.com
//npm.pkg.github.com/:_authToken=YOUR_GITHUB_PAT
Then install:
npm install @hummbl-dev/mcp-serverAdd 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"]
}
}
}Retrieve the canonical Self-Dialectical AI Systems methodology, including all stages and HUMMBL Base120 references.
Example:
{}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.
Retrieve detailed information about a specific mental model.
Example:
{
"code": "P1"
}List all 120 mental models, optionally filtered by transformation type.
Example:
{
"transformation_filter": "P"
}Search models by keyword across names, descriptions, and examples.
Example:
{
"query": "decision"
}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."
}Retrieve information about a specific transformation type and all its models.
Example:
{
"type": "IN"
}Find pre-defined problem patterns with recommended approaches.
Example:
{
"query": "innovation"
}Export a curated subset of Base120 models as Markdown or JSON — useful for docs, decks, or feeding models into another LLM's context. Pass codes for a specific list, transformation for a whole group, or neither for all 120.
Example:
{
"format": "markdown",
"codes": ["P1", "IN3", "CO5", "DE1"]
}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.
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.
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.
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.
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.
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.
Use when: Investigating failures, incidents, or recurring problems Duration: 20-30 minutes Sequence: P → IN → DE → SY
Systematically find root causes, not just symptoms.
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.
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.
List all available guided workflows.
{
"tool": "list_workflows"
}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"
}
}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."
}
}Discover which workflow best fits your problem.
{
"tool": "find_workflow_for_problem",
"arguments": {
"problem_keywords": "system failure production"
}
}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
MCP prompts are user-invocable templates. In Claude Desktop they appear in the "Attach from MCP" menu and can be selected to kick off a conversation. All prompts take a free-text problem argument; apply_model also takes a model_code.
| Prompt | What it does |
|---|---|
root_cause_analysis |
Kicks off the Root Cause Analysis workflow (Perspective → Inversion → Decomposition → Meta-Systems) against your problem. |
strategy_design |
Kicks off the Strategy Design workflow. |
decision_making |
Kicks off the Decision Making workflow. |
analyze_with_models |
Open-ended: calls recommend_models, fetches the top matches, and synthesises them into concrete guidance for your problem. |
apply_model |
Applies one specific model (e.g. P1, IN3) to your problem via its "how to apply" guidance. |
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 frameworkhummbl://methodology/self-dialectical-ai– Structured Self-Dialectical AI methodology definitionhummbl://methodology/self-dialectical-ai/overview– Markdown overview of the methodology for quick operator reference
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.
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.
git clone https://github.com/hummbl-dev/mcp-server.git
cd mcp-server
npm installnpm run buildnpm run devnpm run typechecksrc/
├── 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
The REST API is self-documenting via OpenAPI 3.0. Fetch the spec from:
GET https://api.hummbl.io/openapi.json
Import it into Postman, Insomnia, Hoppscotch, Swagger UI, or any OpenAPI-compatible client. No authentication is required to read the spec.
This repo is part of the HUMMBL cognitive AI architecture. Related repos:
| Repo | Purpose |
|---|---|
| base120 | Authoritative reference for the 120 mental models served by this MCP server |
| hummbl-governance | Governance primitives (kill switch, circuit breaker, cost governor) |
| arbiter | Agent-aware code quality scoring and attribution |
| agentic-patterns | Stdlib-only safety patterns for agentic AI systems |
| aaa | Assured Agentic Architecture — validation-first assurance |
Learn more at hummbl.io.
MIT © HUMMBL, LLC
1.0.0-beta.2