Click on "Install 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., "@Decision OS MCPLog a surprise: the RLS policy blocked the write silently without an error."
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.
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:
Quick Start
1. Install the MCP Server
2. Add to Your Project
Copy the template to your project:
Edit config.yaml with your project name.
3. Configure Cursor
Add to your project's .cursor/mcp.json:
Copy the Cursor rules template:
Tools
Tool | Description |
| Get active case, recent pressures, foundations ranked by relevance, conflicts |
| Log a pressure event when reality differs from expectation |
| Quick-capture a pressure event with minimal friction (only expected + actual required) |
| Create a new case (unit of work) |
| Close a case with outcome signals and regret score (auto-forgets successful cases) |
| Set the active case for the session (persists across restarts) |
| Query foundations from project and global scopes |
| Search past pressure events |
| Check what policy requires for given signals |
| Promote pressure events to a foundation (PROJECT or GLOBAL scope) |
| Elevate a project foundation to global scope |
| Validate that a global foundation applies in current project |
| Review project for unextracted learnings and forgetting opportunities |
| List all cases in the project |
Core Concepts
Pressure Events
The primary learning artifact. Logged when something unexpected happens:
Foundations
Compressed learnings promoted from repeated pressure events:
Hierarchical Foundations (GLOBAL -> PROJECT)
Decision OS supports a cascading scope model similar to Git config:
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:
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:
Directory Structure
LLM Workflow
At task start: Call
get_context()to load active case and foundations (ranked by relevance)When surprised: Call
quick_pressure()for fast capture orlog_pressure()for full detailBefore BUILD decisions: Call
check_policy()to see requirementsAt task end: Call
close_case()with regret scorePeriodically: 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:
Cases are born when work starts
Pressure events are captured when surprises happen
PEs are promoted to foundations when patterns emerge
Cases are forgotten when they have nothing left to teach
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 / HIGHreversibility: EASY / MEDIUM / HARDchange_frequency: RARE / OCCASIONAL / FREQUENTaffected_surface: CORE_DOMAIN / INTEGRATION / DATA_MODEL / INFRA_DEPLOY / SECURITY_BOUNDARY / UI_UX / PERFORMANCE_CRITICALnovelty: LOW / MEDIUM / HIGH
Decisions
approach: REUSE / REFRAME / BUILD / HYBRIDposture: MINIMAL / BALANCED / ROBUSTvalidation_level: BASIC / STANDARD / STRICT
Outcome Signals
regret: 0-3 (0 = would choose same, 3 = strong regret)regressions: NONE / MINOR / MAJOR
Development
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