Give Claude access to 120 validated mental models — for sharper analysis, clearer decision-making, and systematic problem-solving across six cognitive transformations.
Repository health contract: docs/REPO_HEALTH.md
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
Learn more at hummbl.io.
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.
The HUMMBL MCP Server operates locally on your machine and does not collect, transmit, or store any personal data. All processing happens entirely within your local environment.
- The server reads mental models framework data from its internal database
- Problem descriptions and user inputs are processed locally to generate model recommendations
- No data is sent to external servers or third-party services
- No telemetry or analytics are collected
- No persistent storage of user inputs or problem descriptions
- All processing is ephemeral and happens in memory
- No logs or audit trails are retained beyond the current session
- We do not share any data with third parties
- The server does not make network requests except for MCP protocol communication with Claude Desktop
- No data is transmitted to HUMMBL servers or any external services
- No data is retained after processing
- All data is cleared from memory when the server process terminates
- No historical records or user data are stored
For privacy-related questions or concerns, contact: privacy@hummbl.io
Apache 2.0 © HUMMBL, LLC
1.0.0-beta.2
See DOCS/REPO_HEALTH.md for health contract, validation commands, canonical source, and branch-protection expectations.