DecisionNode: CLI and MCP Server for Semantic Decision Storage

What DecisionNode Does

DecisionNode provides persistent decision storage across AI tools through a CLI and MCP server. You record structured decisions as JSON, they get embedded as vectors, and your AI can search and retrieve them via MCP. All decisions for a project (or global decisions) are accessible from every AI tool you use.

Key Features

• Structured decision format: Each decision is JSON with scope, decision, rationale, and constraints
• MCP integration: AI tools can access decisions through the Model Context Protocol
• Semantic search: Decisions are embedded as vectors for semantic retrieval
• Cross-tool compatibility: Works with Claude Code, Cursor, Windsurf, Antigravity, or any MCP client
• Local-only: All data stays on your machine
• Free and open source: MIT licensed

Decision Structure Example

{
  "id": "ui-001",
  "scope": "UI",
  "decision": "Use Tailwind CSS for all styling",
  "status": "active",
  "rationale": "Consistent design tokens, easy for AI to generate correct classes.",
  "constraints": [
    "No arbitrary values (e.g. w-[37px]) unless absolutely necessary"
  ]
}

How It Works

The AI calls search_decisions through MCP when it needs context, instead of running RAG and injecting results into the context window. This provides a more structured alternative to memory.md/claude.md files, with the developer finding DecisionNode to be a useful complement to existing memory systems.

Additional Features

The tool includes agent behavior/search configuration, decision history tracking, conflict detection, and more tools accessible through MCP. The developer built it primarily with Claude Code after starting with Antigravity, finding it particularly useful when switching between different AI tools while maintaining consistent project decisions.