Day 12: The Festival Mascot Crisis
This was the perfect use case for the Council of Mine MCP extension. Nine AI personas, each with a distinct reasoning style, debating and voting democratically.
This challenge focused on using MCP sampling to orchestrate intelligent multi‑agent reasoning inside goose.
What MCP:
MCP, the Model Context Protocol, is a framework that allows tools and extensions to communicate with AI models in a structured, reliable way. Instead of returning static data, an MCP extension can request reasoning from the AI model, incorporate that reasoning into its own logic, and return an intelligent, context‑aware result. This enables extensions to behave like specialized agents that can analyze information, generate multiple perspectives, and support complex decision workflows. MCP sampling builds on this by allowing an extension to create several distinct AI viewpoints, compare them, and synthesize a final recommendation.
The Challenge: Convene the Council
The mission was to install the Council of Mine extension, initiate a debate on the mascot topic, gather nine perspectives, run a democratic vote, and synthesize the final decision. The goal was to demonstrate how MCP sampling enables extensions to request AI reasoning, generate multiple viewpoints, and return structured analysis rather than raw data.
MCP Sampling Overview
MCP sampling allows extensions to request help from goose’s AI model. Instead of returning static data, the extension can ask the model to analyze, interpret, or generate multiple perspectives.
Normal extension flow:
You → goose → Extension → Returns data → goose interprets
MCP sampling flow:
You → goose → Extension → Extension requests AI reasoning
Extension receives AI output → Returns intelligent analysis → goose
This enables extensions to behave like intelligent specialists rather than simple data providers.
Why MCP Sampling Matters
- Extensions can generate multiple AI personas
- Distributed reasoning becomes possible
- Domain expertise can be simulated
- Complex decisions can be debated democratically
- The extension becomes an orchestrator of AI perspectives
Real‑World Applications
- Multi‑perspective analysis
- Intelligent documentation
- Context‑aware search
- Database analysis
- Multi‑expert code review
Submissions and Debates Completed A total of six debates were conducted across simple and complex topics. All required tasks for Day 12 were completed.
This challenge demonstrated how Council of Mine leverages MCP sampling to create 9 distinct AI personas that debate topics and vote democratically, producing nuanced recommendations superior to single-perspective analysis.
Debates Conducted: 6 Total
Simple Topics (4):
Mascot Selection → Winner: The Systems Thinker → Penguin
Mascot Naming → Winner: The Pragmatist → Test 2-3 finalists with community
Sidekick Decision → Winners: Visionary & Systems Thinker → Pilot program
Cookie Selection → Winners: Mediator & Visionary → Curated selection with gingerbread anchor
Complex Topics (2):
Festival Expansion → Winner: The Pragmatist → Add 1 day first, measure, then decide
Tradition vs Innovation → Winners: Analyst & Pragmatist → 60/40 ratio with data validation
Most Influential Council Members:
The Pragmatist: 11 votes (dominates complex decisions)
The Systems Thinker: 10 votes (scales with complexity)
The Analyst: 5 votes (data-driven validation)
Universal Patterns:
Evidence beats ideology every time
Testing before commitment is universal
Accessibility matters in all decisions
60/40 tradition/innovation ratio emerged naturally
Incremental approaches consistently win
MCP Sampling Power:
9 distinct AI personas with unique reasoning
Democratic voting reveals collective wisdom
Synthesis combines best of all perspectives
Superior to single-perspective analysis
Complex decision-making requires:
Systems-level thinking to understand interconnections
Pragmatic approaches to manage risk
Evidence-based validation before commitment
Incremental implementation for testing assumptions
Accessibility considerations for inclusive outcomes
These members consistently shaped outcomes, especially in complex decision spaces.
Universal Patterns Observed
- Evidence consistently outperformed ideology
- Testing before commitment was preferred
- Accessibility considerations appeared in every decision
- A natural sixty to forty tradition to innovation ratio emerged
- Incremental approaches were favored over large‑scale changes
MCP Sampling Capabilities Demonstrated
The Council of Mine extension showcased the strengths of MCP sampling:
- Nine distinct AI personas with unique reasoning styles
- Democratic voting that reveals collective intelligence
- Synthesis that merges the strongest elements of each viewpoint
- Superior decision quality compared to single‑perspective analysis
Requirements for Complex Decision Making
The council demonstrated that effective complex decisions require:
- Systems‑level thinking to understand interdependencies
- Pragmatic approaches to reduce risk
- Evidence‑based validation before committing resources
- Incremental implementation to test assumptions
- Accessibility considerations to ensure inclusive outcomes
Across all debates, the council consistently showed that the strongest decisions emerge from:
- Recognizing multiple valid perspectives
- Testing assumptions before scaling
- Balancing innovation with proven methods
- Preserving core values while enabling evolution
- Using data to guide decisions rather than justify them
Visualizations Created
Interactive Visualizations Created
Council Voting Power (Bar Chart)
Shows total votes received by each council member
The Pragmatist leads with 11 votes
Systems Thinker close behind with 10 votes
Reveals influence hierarchy across all debates
Decision Characteristics (Radar Chart)
Compares simple vs complex topic patterns
Complex topics score 95% on evidence-based approaches
Complex topics heavily favor systems thinking
Shows how complexity shifts decision-making priorities
Challenge Flow (Sankey Diagram)
Visualizes flow from challenge levels through debates to outcomes
Demonstrates how beginner work feeds intermediate analysis
Shows convergence on evidence-based outcomes and testing frameworks
Illustrates incremental approach emerging from advanced challenges
Topic & Strategy Distribution (Donut Charts)
67% simple topics, 33% complex topics
Pragmatic strategies dominate at 35%
Systems-based and data-driven approaches folllow
Balanced strategies round out decision-making
Member Influence Trajectory (Line Chart)
Tracks how council member votes change across debates
Pragmatist influence surges in complex topics
Systems Thinker maintains steady high influence
Visionary shows consistent moderate influence
Outcomes Treemap
Visual hierarchy of all achievements
Color-coded by type (Decision, Process, Strategy, Analysis, Output)
Size represents relative importance
Shows balanced completion across all levels
Insights
This challenge demonstrated how multi‑agent reasoning can outperform single‑agent decision making. MCP sampling enables extensions to orchestrate multiple AI perspectives, debate complex topics, and produce structured, evidence‑driven recommendations. The Council of Mine extension is a practical example of distributed AI reasoning applied to real decision workflows.
Final Thoughts
Day 12 showcased the power of MCP sampling and multi‑persona reasoning. By convening the Council of Mine, I transformed a chaotic debate into a structured, democratic decision process. The result was a clear, evidence‑driven recommendation supported by nine distinct reasoning styles.
Day 12: Completed Mascot crisis: Resolved. Council decision: Selected.
This post is part of my Advent of AI journey, AI Engineering: Advent of AI with goose Day 12.
Follow along for more AI Engineering Adventures with Eri!
Top comments (0)