Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mcp-decision-lab Help me decide between Postgres and DynamoDB for our backend."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP server for structured decision-making — weighted decision matrices, criteria scoring, sensitivity analysis and a defensible recommendation.
LLMs are good at listing pros and cons and then picking whatever "feels" right. That reasoning is opaque, unstable, and impossible to audit: change one adjective in the prompt and the answer flips. mcp-decision-lab is a thinking tool — the model uses it to structure its own reasoning as an explicit weighted decision matrix. Every score requires a written rationale, weights are normalized and transparent, and the final analyze step does real math: exact (closed-form, not brute-force) sensitivity analysis that tells you which criterion weight would flip the winner and at what value — so the recommendation comes with a robustness verdict instead of vibes.
Related MCP server: mcpdeployment
Tool | Arguments | Returns |
|
| New |
|
| The recorded cell, remaining missing cells, next step |
|
| Full matrix: raw scores, effective scores, weighted scores, per-option totals, rationales, missing cells |
|
| Ranking + margin, per-criterion sensitivity (exact flip weight, direction, new winner, decisive flag), strengths/weaknesses per option, robustness verdict ( |
| — | All sessions with status: |
Criteria where a high raw score is bad (cost, risk, complexity) take "higher_is_better": false — the effective score becomes 10 - score automatically.
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]The sensitivity math. With normalized weights, changing criterion c's weight from w to w′ (renormalizing the others proportionally) makes every option's total linear in w′. For the winner A vs a challenger B the gap is gap(w′) = w′·d + (1−w′)·r, where d is their effective-score difference on c and r is their weight-scaled difference on everything else. Solving gap(w′) = 0 gives the exact flip weight w* = r/(r−d) — reported only if a region of [0, 1] exists where the challenger strictly wins. If no ±50% relative change to any single weight flips the winner, the verdict is robust; otherwise fragile, naming the criteria the decision hinges on.
No install needed — uvx fetches and runs it:
Claude Desktop (claude_desktop_config.json):
{
"mcpServers": {
"decision-lab": {
"command": "uvx",
"args": ["mcp-decision-lab"]
}
}
}Claude Code:
claude mcp add decision-lab -- uvx mcp-decision-labPrefer a permanent install? pip install mcp-decision-lab, then use mcp-decision-lab as the command.
Sessions persist as JSON in ~/.mcp-decision-lab/decisions.json (override the directory with the DECISION_LAB_DIR environment variable).
User: Help me pick a database for the new SaaS backend — Postgres, MongoDB or DynamoDB. Cost matters most, then scalability, then how well the team knows it.
The model starts a session:
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 ..."}Then scores all 9 cells, each with a rationale:
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') ..."}And analyzes:
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.)"
}Model (to user): Postgres wins at 7.1 weighted vs DynamoDB's 5.8, and the result is robust: no reasonable (±50%) shift in any single weight changes the winner. It would take valuing scalability at ~0.47 instead of 0.3 — or cost dropping below ~0.12 — for DynamoDB to overtake. If you genuinely expect hockey-stick scale, revisit; otherwise Postgres is the defensible choice.
git clone https://github.com/AleBrito124356/mcp-decision-lab
cd mcp-decision-lab
pip install -e ".[dev]"
python -m pytestTests exercise mcp_decision_lab/core.py only and run without the mcp package installed. To run the server straight from a source checkout: python -m mcp_decision_lab.server.
Part of a family of small, dependency-light MCP servers:
mcp-devils-advocate — stress-test a claim: devil's advocate, premortem, assumption audits
mcp-secret-sentinel — scan code for exposed secrets, always redacted
mcp-git-historian — churn hotspots, blame summaries, bus factor
mcp-memory-vault — persistent memory with SQLite FTS5 search
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