flowchart TD
    A[start_decision<br/>question + options + weighted criteria] --> B[weights normalized to sum 1.0<br/>session dec-xxxx persisted to disk]
    B --> C[score_option x N<br/>one cell = option x criterion,<br/>score 0-10 + mandatory rationale]
    C -->|cells missing| C
    C -->|matrix complete| D[get_matrix<br/>review raw / weighted scores]
    D --> E[analyze]
    E --> F[ranking + margin<br/>weighted totals]
    E --> G["sensitivity analysis<br/>closed-form flip weight per criterion:<br/>solve gap(w') = w'·d + (1-w')·r = 0"]
    E --> H[strengths / weaknesses<br/>best and worst criterion per option]
    F --> I[robustness verdict<br/>robust vs fragile under ±50% weight shifts]
    G --> I
    H --> I
    I --> J[defensible recommendation]

{
  "mcpServers": {
    "decision-lab": {
      "command": "uvx",
      "args": ["mcp-decision-lab"]
    }
  }
}

claude mcp add decision-lab -- uvx mcp-decision-lab

start_decision(
    question="Which database should we use for the new SaaS backend?",
    options=["Postgres", "MongoDB", "DynamoDB"],
    criteria=[
        {"name": "cost", "weight": 0.5, "higher_is_better": False},
        {"name": "scalability", "weight": 0.3},
        {"name": "team-familiarity", "weight": 0.2},
    ],
)
# → {"decision_id": "dec-34b5", "cells_total": 9,
#    "next_step": "Score each option against each criterion using score_option ..."}

score_option("dec-34b5", "Postgres", "cost", 3,
    "Managed Postgres (RDS/Neon) is cheap and predictable at our scale")
score_option("dec-34b5", "DynamoDB", "scalability", 10,
    "Effectively unlimited managed horizontal scale")
score_option("dec-34b5", "DynamoDB", "team-familiarity", 4,
    "Only one engineer knows single-table design patterns")
# ... 6 more cells ...
# → {"missing_cells": [], "next_step": "Matrix complete — call analyze('dec-34b5') ..."}

analyze("dec-34b5")

{
  "ranking": [
    {"rank": 1, "option": "Postgres", "total": 7.1},
    {"rank": 2, "option": "DynamoDB", "total": 5.8},
    {"rank": 3, "option": "MongoDB", "total": 5.6}
  ],
  "winner": "Postgres",
  "margin": 1.3,
  "sensitivity": [
    {"criterion": "cost", "weight": 0.5, "decisive": true,
     "flip": {"flip_weight": 0.1176, "direction": "decrease",
              "new_winner": "DynamoDB", "within_50pct_band": false}},
    {"criterion": "scalability", "weight": 0.3, "decisive": true,
     "flip": {"flip_weight": 0.4717, "direction": "increase",
              "new_winner": "DynamoDB", "within_50pct_band": false}},
    {"criterion": "team-familiarity", "weight": 0.2, "decisive": false, "flip": null}
  ],
  "decisive_criteria": ["cost", "scalability"],
  "robustness": "robust",
  "recommendation": "Choose Postgres (7.1 weighted) over DynamoDB (5.8); margin 1.3. Postgres is strongest on 'team-familiarity' (9/10) and weakest on 'scalability' (6/10). The result is robust: no ±50% relative change to any single criterion weight changes the winner. (Extreme shifts could still flip it: 'cost' would have to decrease from 0.5 to 0.1176 for DynamoDB to win; 'scalability' would have to increase from 0.3 to 0.4717 for DynamoDB to win.)"
}

git clone https://github.com/AleBrito124356/mcp-decision-lab
cd mcp-decision-lab
pip install -e ".[dev]"
python -m pytest