criterium-mcp 0.1.1

MCP Server for multi-criteria decision analysis (AHP, TOPSIS, Sensitivity, Decision Trees)

criterium-mcp

Multi-criteria decision analysis tools for LLM agents via the Model Context Protocol (MCP).

Provides four complementary methods that share the same data model (criteria weights + alternative scores), letting the LLM orchestrate cross-validation:

Method	What it does
AHP	Pairwise comparison → priority weights + consistency ratio (CR)
TOPSIS	Distance-to-ideal ranking (Euclidean in weighted space)
Sensitivity	Weight perturbation to find critical ranking transitions
Decision Tree	Sequential decisions with multi-criteria leaf outcomes

Quick start

# Install from PyPI
uv tool install criterium-mcp

# Or run directly
uvx criterium-mcp

MCP configuration

Add to your MCP client config (e.g. claude_desktop_config.json, roo-code.json, etc.):

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

Tools

get_saaty_scale()

Returns the Saaty 1-9 scale reference table for pairwise comparisons.

compute_ahp_weights(pairwise_matrix)

Computes priority weights from an n×n pairwise comparison matrix using Saaty's eigenvector method. Reports:

• Weights: normalized priority vector (sum = 1)
• λ_max: principal eigenvalue
• CI: consistency index
• CR: consistency ratio (Saaty's RI table included)
• Consistent?: CR < 0.10 verdict

score_alternatives_ahp(weights, criteria_minimize, alternatives)

Scores and ranks alternatives using AHP weights with min-max normalization. Handles both maximize and minimize criteria (e.g. "cost" → minimize, "quality" → maximize).

evaluate_topsis(weights, criteria_minimize, alternatives)

TOPSIS ranking: finds the alternative closest to the ideal solution and farthest from the nadir. Provides an alternative perspective to the AHP weighted sum — if both methods agree, the decision is more robust.

sensitivity_analysis(base_weights, criteria_minimize, alternatives, steps=20)

Systematically varies each criterion weight from 0 to 1 and tracks ranking changes. Reports critical weight thresholds where the top alternative flips — essential for identifying fragile recommendations.

evaluate_decision_tree(tree, weights, criteria_minimize)

Evaluates a sequential decision tree where leaf outcomes are multi-criteria score vectors. Three node types:

• decision: you choose (maximize utility)
• chance: nature chooses (expected utility with probabilities)
• leaf: terminal outcome with per-criterion scores

Returns the optimal path and the full tree annotated with utilities at every node.

Development

git clone https://github.com/yohann/criterium-mcp
cd criterium-mcp
uv venv
source .venv/bin/activate
uv pip install -e .
criterium

License

MIT