SDOF MCP - Structured Decision Optimization Framework

@tgf-between-your-legs

リポジトリ

基本情報

カテゴリ

開発者ツール

ライセンス

MIT license

トランスポート

stdio

作者

tgf-between-your-legs

SDOF MCP - Structured Decision Optimization Framework について

Structured Decision Optimization Framework (SDOF) MCP Server - Next-generation knowledge management with 5-phase optimization workflow

#sdof#decision-optimization#knowledge-management

SDOF MCP - Structured Decision Optimization Framework の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "sdof-mcp": {
      "command": "node",
      "args": [
        "build/test-unified-system.js"
      ]
    }
  }
}

SDOF MCP - Structured Decision Optimization Framework のツール

ツール一覧はまだありません

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ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is SDOF MCP - Structured Decision Optimization Framework?

SDOF MCP - Structured Decision Optimization Framework is a Model Context Protocol (MCP) server that provides persistent memory and context management for AI systems through a structured 5-phase optimization workflow. It is built for developers and AI practitioners who need to store, retrieve, and optimize knowledge with semantic search, vector embeddings, and multiple content types. It supports both MCP tools and an HTTP API, using OpenAI for embeddings and MongoDB or SQLite for storage.

How to use SDOF MCP - Structured Decision Optimization Framework?

Clone the repository, install dependencies with npm install, build with npm run build, set the OPENAI_API_KEY environment variable, and start the server with npm start. Then configure your MCP‑compatible client (e.g., Claude Desktop) to use the server with the stdio transport and the store_sdof_plan tool. Optionally, set environment variables like EMBEDDING_MODEL, HTTP_PORT, or MONGODB_URI.

Key features of SDOF MCP - Structured Decision Optimization Framework

5‑phase optimization workflow (Exploration, Analysis, Implementation, Evaluation, Integration)
Vector embeddings with OpenAI for semantic search
Persistent storage with MongoDB/SQLite and vector indexing
Prompt caching optimized for LLM efficiency
Schema validation for structured content types
Multi‑interface: MCP tools and HTTP API (port 3000)

Use cases of SDOF MCP - Structured Decision Optimization Framework

Storing decision records and rationales for architecture choices
Saving code implementations and examples with metadata
Capturing analysis results and findings for later retrieval
Managing evaluation reports and performance metrics
Consolidating integration documentation and learning artifacts

よくある質問

What are the prerequisites for running SDOF MCP - Structured Decision Optimization Framework?

Node.js 18+ is required. You also need an OpenAI API key for embeddings and a MCP‑compatible client (such as Claude Desktop).

What databases are supported?

MongoDB and SQLite are supported for persistent storage with vector indexing. MongoDB URI is configured via the `MONGODB_URI` environment variable; by default SQLite is used.

What is the primary MCP tool provided?

The primary tool is `store_sdof_plan`. It accepts a `plan_content` string (Markdown) and a `metadata` object with fields like `planTitle`, `planType`, `tags`, `phase`, and `cache_hint`.

What content types can be stored?

Supported content types are: `text`, `code`, `decision`, `analysis`, `solution`, `evaluation`, and `integration`.

How do I configure the MCP client?

Add a `stdio` server entry to your MCP client configuration with the command `node path/to/sdof-mcp/build/index.js` and the `OPENAI_API_KEY` environment variable. Use `alwaysAllow` to auto‑approve the `store_sdof_plan` tool.

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