Decision OS MCP

Decision OS MCP

An LLM-native decision tracking system that captures unexpected engineering outcomes as 'pressure events' to build a persistent learning foundation. It enables AI assistants to manage cases, log surprises, and promote recurring insights into global or project-specific knowledge bases.

Category
Visit Server

README

Decision OS MCP

An MCP server for Decision OS — an LLM-native decision tracking and learning system.

What is Decision OS?

Decision OS captures novel pressure — moments when reality surprises you during engineering work. Unlike traditional documentation, it focuses on what an LLM couldn't predict, creating a learning loop:

Cases → Pressure Events (surprises) → Outcomes → Foundations (compressed learnings)

Quick Start

1. Install the MCP Server

# Global install
npm install -g decision-os-mcp

# Or use npx (no install needed)
npx decision-os-mcp

2. Add to Your Project

Copy the template to your project:

cp -r templates/.decision-os /path/to/your-project/

Edit config.yaml with your project name.

3. Configure Cursor

Add to your project's .cursor/mcp.json:

{
  "mcpServers": {
    "decision-os": {
      "command": "npx",
      "args": ["-y", "decision-os-mcp"],
      "env": {
        "DECISION_OS_PATH": "${workspaceFolder}/.decision-os"
      }
    }
  }
}

Copy the Cursor rules template:

cp templates/.cursor/rules/decision-os.mdc /path/to/your-project/.cursor/rules/

Tools

Tool Description
get_context Get active case, recent pressures, foundations ranked by relevance, conflicts
log_pressure Log a pressure event when reality differs from expectation
quick_pressure Quick-capture a pressure event with minimal friction (only expected + actual required)
create_case Create a new case (unit of work)
close_case Close a case with outcome signals and regret score (auto-forgets successful cases)
set_active_case Set the active case for the session (persists across restarts)
get_foundations Query foundations from project and global scopes
search_pressures Search past pressure events
check_policy Check what policy requires for given signals
promote_to_foundation Promote pressure events to a foundation (PROJECT or GLOBAL scope)
elevate_foundation Elevate a project foundation to global scope
validate_foundation Validate that a global foundation applies in current project
suggest_review Review project for unextracted learnings and forgetting opportunities
list_cases List all cases in the project

Core Concepts

Pressure Events

The primary learning artifact. Logged when something unexpected happens:

expected: "Supabase insert would throw on null FK"
actual: "RLS silently blocked the write, no error"
adaptation: "Added explicit null-check before insert"
remember: "Supabase RLS fails silently on null FK values"

Foundations

Compressed learnings promoted from repeated pressure events:

id: F-0001
title: "Supabase RLS fails silently on null FK"
default_behavior: "Always validate FK values before insert when using RLS"
context_tags: [SUPABASE, RLS, DATA_MODEL]
confidence: 2  # Out of 3
scope: PROJECT  # or GLOBAL
origin_project: my-project
validated_in: [my-project, other-project]
exit_criteria: "Supabase adds explicit error for null FK violations"
source_pressures: [PE-0003, PE-0007]

Hierarchical Foundations (GLOBAL -> PROJECT)

Decision OS supports a cascading scope model similar to Git config:

~/.decision-os/                    # GLOBAL (user-wide, universal learnings)
├── config.yaml
└── defaults/foundations.yaml      # GF-prefixed foundations

~/projects/my-app/.decision-os/    # PROJECT (specific to this codebase)
├── config.yaml
├── cases/
└── defaults/foundations.yaml      # F-prefixed foundations

Resolution order: PROJECT wins over GLOBAL on conflicts.

Global foundations are recommendations, not rules. They represent universal patterns that transcend specific tech stacks:

  • Tool behaviors (e.g., "MCP descriptor paths may be stale")
  • Debugging strategies (e.g., "Trace call sites before refactoring")
  • Meta-learnings (e.g., "Question requirements before implementing")

Setup global foundations:

# Create global .decision-os
mkdir -p ~/.decision-os/defaults
cp templates/global-.decision-os/config.yaml ~/.decision-os/
cp templates/global-.decision-os/defaults/foundations.yaml ~/.decision-os/defaults/

Conflict detection: When get_context is called, it highlights conflicts where project and global foundations overlap or contradict each other.

Cases

Bounded units of work (feature, bugfix, spike) that provide context for pressure events:

id: 0001-add-tile-caching
title: "Add tile caching"
goal: "Reduce API latency for repeated tile requests"
status: ACTIVE
signals:
  context:
    risk_level: MEDIUM
    affected_surface: [PERFORMANCE_CRITICAL, INTEGRATION]
decisions:
  approach: BUILD
  posture: BALANCED
  validation_level: STANDARD

Directory Structure

# Global (user-wide)
~/.decision-os/
├── config.yaml               # scope: GLOBAL
└── defaults/
    └── foundations.yaml      # GF-prefixed universal learnings

# Project (per-codebase)
your-project/
├── .decision-os/
│   ├── config.yaml           # scope: PROJECT
│   ├── cases/
│   │   ├── 0001-bootstrap/
│   │   │   ├── case.yaml     # Case metadata
│   │   │   └── pressures.yaml # Pressure events
│   │   └── 0002-add-auth/
│   │       └── ...
│   └── defaults/
│       └── foundations.yaml  # F-prefixed project learnings
├── .cursor/
│   ├── mcp.json              # MCP server config
│   └── rules/
│       └── decision-os.mdc   # LLM instructions
└── src/

LLM Workflow

  1. At task start: Call get_context() to load active case and foundations (ranked by relevance)
  2. When surprised: Call quick_pressure() for fast capture or log_pressure() for full detail
  3. Before BUILD decisions: Call check_policy() to see requirements
  4. At task end: Call close_case() with regret score
  5. Periodically: Call suggest_review() to find unextracted learnings and forgetting opportunities

Forgetting

The system forgets by design. Cases are temporary containers — knowledge lives in foundations.

When close_case() is called with regret 0 and there are no unpromoted pressure events, the case is automatically deleted. Not archived. Forgotten.

This keeps the .decision-os/cases/ directory lean: only cases that still have uncompressed learning (unpromoted PEs or regret 1+) survive.

The lifecycle:

  1. Cases are born when work starts
  2. Pressure events are captured when surprises happen
  3. PEs are promoted to foundations when patterns emerge
  4. Cases are forgotten when they have nothing left to teach
  5. Foundations survive as the only persistent knowledge

Use suggest_review() to find cases blocking forgetting (regret 0 but unpromoted PEs remain) and decide whether to promote or discard them.

Active Case Persistence

The active case is persisted to .decision-os/.active-case and survives MCP server restarts. No more losing your active case when Cursor restarts.

Signals Vocabulary

Context Signals (before execution)

  • risk_level: LOW / MEDIUM / HIGH
  • reversibility: EASY / MEDIUM / HARD
  • change_frequency: RARE / OCCASIONAL / FREQUENT
  • affected_surface: CORE_DOMAIN / INTEGRATION / DATA_MODEL / INFRA_DEPLOY / SECURITY_BOUNDARY / UI_UX / PERFORMANCE_CRITICAL
  • novelty: LOW / MEDIUM / HIGH

Decisions

  • approach: REUSE / REFRAME / BUILD / HYBRID
  • posture: MINIMAL / BALANCED / ROBUST
  • validation_level: BASIC / STANDARD / STRICT

Outcome Signals

  • regret: 0-3 (0 = would choose same, 3 = strong regret)
  • regressions: NONE / MINOR / MAJOR

Development

# Install dependencies
npm install

# Build
npm run build

# Run locally
DECISION_OS_PATH=/path/to/.decision-os npm start

Philosophy

  • Log only novel pressure: Don't document what an LLM could derive
  • The system should forget: Successful cases are deleted. Knowledge lives in foundations, not cases
  • Hypotheses, not axioms: Foundations have confidence and can be revised
  • Minimal ceremony: Small vocabulary, structured but not bureaucratic
  • Capture first, filter later: When unsure, log it — capturing too much is better than missing surprises
  • LLM-native: Designed for AI-assisted engineering workflows

License

MIT

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured