HUMMBL MCP Server

# HUMMBL Case Study #1: Framework-Driven Product Development **Subject:** Reuben Bowlby, Chief Engineer, HUMMBL LLC **Duration:** 22+ months (January 2024 – November 2025) **Domain:** Cognitive framework development, AI-assisted product engineering **Outcome:** Production-ready Base120 mental models system deployed at hummbl.io --- ## Executive Summary A solo founder used the HUMMBL Base120 mental models framework to architect, validate, and deploy the framework itself—a meta-recursive application demonstrating the system's power. Over 18 months, the project evolved from a 42-model prototype to a complete 120-model cognitive system, achieved 9.2/10 validation quality, and coordinated 4+ AI agents in parallel execution workflows. The framework now serves as both product and methodology. --- ## The Challenge **Starting Point:** A collection of mental models used informally for problem-solving, with no systematic organization, validation methodology, or production infrastructure. **Complexity Factors:** - Solo founder with competing time demands (fitness training, government contracts, consulting) - No existing product—building the plane while flying it - Need for empirical validation, not just theoretical completeness - Multi-system coordination across AI platforms with different capabilities - Technical infrastructure (API, MCP server, web deployment) required alongside content **Wickedness Score:** 20/30 (Tier 4 - Wicked Problem) - 6+ significant variables - Multiple stakeholder types (developers, researchers, practitioners) - Low predictability of market reception - High interdependency between models - Partially reversible decisions --- ## The HUMMBL Approach ### Phase 1: Foundation (Jan–Jun 2024) **Models Applied:** - **DE3 Modularization** — Established 6-transformation architecture (P, IN, CO, DE, RE, SY) - **P1 First Principles** — Defined what a "mental model" actually is vs. heuristics/frameworks - **CO8 Layered Abstraction** — Created Base-N scaling system (Base6 → Base120) **Key Decision:** Base42 (6×7 models) identified as "practical optimum" for wicked problems. This became the validation benchmark—if Base42 couldn't solve a Tier 4 problem, the framework had gaps. **Outcome:** Formal architecture established. Priority rankings assigned to all models. --- ### Phase 2: Expansion (Jul–Sep 2024) **Models Applied:** - **RE4 Iterative Refinement** — Expanded from Base42 → Base90 based on practitioner feedback - **SY19 Meta-Model Selection** — Developed criteria for when to use which Base-N level - **IN7 Boundary Analysis** — Defined transformation interdependencies and information flow **Key Decision:** Formalized operator algebra for model composition. Models don't operate in isolation—systematic combination creates emergent analytical power. **Outcome:** Base90 complete with formal language specification. Bernard Analytical Agent prototype built. --- ### Phase 3: Validation (Oct 2024) **Models Applied:** - **DE7 Root Cause Analysis** — Identified why previous validation attempts felt incomplete - **SY18 Measurement & Telemetry** — Created 5-question wickedness scoring rubric - **RE6 Feedback Loops** — Established quality gates with automated checking **Pivot Point:** Shifted from subjective tier classification to quantitative scoring: | Dimension | Score Range | |-----------|-------------| | Variables | 0-5 | | Stakeholders | 0-5 | | Predictability | 0-5 | | Interdependencies | 0-5 | | Reversibility | 0-5 | | **Total** | **0-30** | **Tier Mapping:** - Tier 1 (Simple): 1-9 points - Tier 2 (Moderate): 10-14 points - Tier 3 (Complex): 15-19 points - Tier 4 (Wicked): 20-24 points - Tier 5 (Super-Wicked): 25-30 points **Outcome:** Empirical validation methodology. Base-N coverage testing against real problems. --- ### Phase 4: Productization (Oct–Nov 2024) **Models Applied:** - **CO12 Standardization** — Unified model format (code, name, definition, example) - **SY20 Systems-of-Systems Coordination** — Multi-agent orchestration protocols - **P8 Stakeholder Mapping** — Identified user segments (developers via MCP, researchers via API, practitioners via web) **Multi-Agent Coordination Breakthrough:** Developed SITREP protocol for parallel AI execution: | Agent | Role | Capabilities | |-------|------|--------------| | Claude Sonnet 4.5 | Lead Architect | Strategic planning, documentation, orchestration | | ChatGPT-5 | Validator | Quality assurance, gap analysis, verification gates | | Windsurf Cascade | Executor | Code implementation, environment management | | Cursor | Specialist | Direct code execution, real-time debugging | **Protocol Features:** - Military-style situation reports for unambiguous communication - Authorization codes for autonomous execution boundaries - Parallel task assignment with merge gates - Quality checkpoints before phase transitions **Outcome:** 120/120 models validated at 9.2/10 average quality. Production deployment at hummbl.io. --- ### Phase 5: Infrastructure (Nov 2024 – Present) **Models Applied:** - **DE3 Modularization** — Separated concerns: web UI, API, MCP server - **SY18 Measurement & Telemetry** — Built chaos engineering test suite - **RE7 Continuous Integration** — Automated quality gates, nightly regression **Technical Deliverables:** | Component | Status | Metrics | |-----------|--------|---------| | Web UI (hummbl.io) | Production | React + Cloudflare | | MCP Server | Production | 140 chaos tests, 100% pass | | API Layer | In Progress | Cloudflare Workers + D1 | | Documentation | Complete | 6 tools, full schema | --- ## Results ### Quantitative Outcomes | Metric | Value | |--------|-------| | Models Validated | 120/120 (100%) | | Quality Score | 9.2/10 average | | Test Coverage | 140 chaos tests | | Pass Rate | 100% | | Development Time | 18 months | | Team Size | 1 human + 4 AI agents | ### Qualitative Outcomes Framework Self-Validation: The project proved Base120 can handle Tier 4 (Wicked) problems. The framework was used to build itself—meta-recursive validation. Multi-Agent Scalability: SITREP protocol demonstrated 4x parallel execution without conflicts. Communication clarity eliminated rework from misalignment. Sustainable Velocity: Solo founder maintained progress alongside full-time job and other obligations through systematic decomposition and agent delegation. ### Evidence References - **Validation Issues:** 120 GitLab issues with embedded evidence blocks (see `gitlab.com/hummbl/base120-validation/issues`). - **Quality Rubric:** Wickedness scoring methodology documented in the HUMMBL validation rubric (`docs/validation/wickedness-rubric.md`). - **Chaos Test Suite:** MCP server chaos tests located at `tests/chaos-runner.js` and `tests/chaos-runner-extended.js`. --- ## Key Learnings ### What Worked 1. **Base-N Scaling** — Not every problem needs Base120. Matching complexity to problem tier prevented over-engineering. 2. **Quantitative Validation** — The 5-question wickedness rubric replaced subjective judgment with reproducible scoring. 3. **Multi-Agent Coordination** — SITREP protocol turned AI tools from assistants into autonomous team members with defined responsibilities. 4. **Quality Gates** — Automated checks (metrics validation, citation linting, test suites) caught issues before they compounded. ### What Would Change 1. **Earlier MCP Investment** — The Model Context Protocol server should have been built sooner to enable AI-native distribution. 2. **User Testing Timing** — Production deployment happened before user acquisition infrastructure. Should have parallelized. 3. **Documentation-First** — Some architectural decisions were made before being documented, requiring reconstruction later. --- ## Models Used (Summary) | Transformation | Models Applied | Primary Use | |----------------|----------------|-------------| | P (Perspective) | P1, P8 | Problem framing, stakeholder identification | | IN (Inversion) | IN7 | Boundary analysis, constraint mapping | | CO (Composition) | CO8, CO12 | Architecture design, standardization | | DE (Decomposition) | DE3, DE7 | Modularization, root cause analysis | | RE (Recursion) | RE4, RE6, RE7 | Iteration, feedback loops, CI/CD | | SY (Systems) | SY18, SY19, SY20 | Measurement, meta-selection, coordination | --- ## Conclusion HUMMBL Base120 successfully powered its own development—a rigorous test of framework validity. The combination of systematic mental models, quantitative validation, and multi-agent coordination enabled a solo founder to build a production-grade cognitive system in 18 months. **The meta-recursive proof:** If a framework can build itself, it can build anything at equivalent complexity. --- ## Next Steps 1. **User Acquisition** — Deploy viral marketing infrastructure, target 10 WAU 2. **Case Studies #2-3** — Document external user applications 3. **API Productization** — Complete Cloudflare Workers deployment 4. **Community Building** — MCP marketplace submissions, developer documentation --- Case Study v1.0 | November 2025 | HUMMBL LLC