README - 🧠 Simple MCP Server with Node.js by takehisa10098

🧠 Simple MCP Server with Node.js

This is a simple prototype of a Model Context Protocol (MCP) server built with Node.js and Express.
It interacts with OpenAI's API to simulate a context-aware assistant—for example, one that helps write stories.

🚀 What You Can Do

Structure your context with system, user, steps, and resources
Call OpenAI API to generate story plots using the context
Add characters dynamically via API
See how changing context affects LLM responses

📦 Requirements

Node.js v18+
npm
OpenAI API key (Get one here)

🛠 Setup

1. Clone this repository

git clone https://github.com/takehisa10098/plot-mcp-server.git
cd plot-mcp-server

2. Install dependencies

npm install

3. Create `.env` file

OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxx

4. Create `context.json` file

{
  "system": "あなたは小説執筆のサポートAIです。",
  "user": {
    "goal": "和風ファンタジーの短編小説を書きたい",
    "constraints": ["文字数は3000字以内", "テーマは『喪失と再生』"]
  },
  "steps": [
    { "name": "キャラクター作成", "status": "done" },
    { "name": "プロット生成", "status": "in_progress" }
  ],
  "resources": {
    "characters": [
      { "name": "アカネ", "role": "主人公", "trait": "寡黙で芯が強い" }
    ],
    "world": "戦国時代風の異世界。陰陽術が存在する。"
  }
}

5. Create `server.js`

const express = require('express');
const fs = require('fs');
const dotenv = require('dotenv');
const { getPlotSuggestion } = require('./openai');

dotenv.config();
const app = express();
app.use(express.json());

const PORT = 3000;
const CONTEXT_PATH = './context.json';

const readContext = () => JSON.parse(fs.readFileSync(CONTEXT_PATH, 'utf-8'));

app.get('/context', (req, res) => {
  const context = readContext();
  res.json(context);
});

app.post('/step/plot', async (req, res) => {
  const context = readContext();
  const plot = await getPlotSuggestion(context);
  res.json({ plot });
});

app.post('/update/characters', (req, res) => {
  const context = readContext();
  const newCharacter = req.body;

  if (!newCharacter.name || !newCharacter.role || !newCharacter.trait) {
    return res.status(400).json({ error: 'name, role, and trait are required' });
  }

  context.resources.characters.push(newCharacter);
  fs.writeFileSync(CONTEXT_PATH, JSON.stringify(context, null, 2), 'utf-8');
  res.json({ message: 'Character added', character: newCharacter });
});

app.listen(PORT, () => {
  console.log(`MCP server running at http://localhost:${PORT}`);
});

6. Create `openai.js`

const { OpenAI } = require('openai');
require('dotenv').config();

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});

async function getPlotSuggestion(context) {
  const { system, user, resources } = context;

  const messages = [
    { role: 'system', content: system },
    {
      role: 'user',
      content: `
目標: ${user.goal}
制約: ${user.constraints.join(', ')}

キャラクター: ${JSON.stringify(resources.characters)}
世界観: ${resources.world}

これを踏まえて、小説のプロット案を出してください。
      `
    }
  ];

  const res = await openai.chat.completions.create({
    model: 'gpt-4-turbo',
    messages,
  });

  return res.choices[0].message.content;
}

module.exports = { getPlotSuggestion };

🧪 How to Use

Start the server

node server.js

Get current context

curl http://localhost:3000/context

Generate a plot

curl -X POST http://localhost:3000/step/plot

Add a new character

curl -X POST http://localhost:3000/update/characters \
  -H "Content-Type: application/json" \
  -d '{
    "name": "ユキ",
    "role": "謎の旅人",
    "trait": "静かで何かを知っているような雰囲気"
  }'

💡 What is MCP?

Model Context Protocol (MCP) is a proposed format for structuring contextual information for LLMs.
It allows applications to define system, user, steps, and resources that represent the ongoing state of a conversation or project.

This project shows how you can design an LLM interaction by modifying structured context, instead of rewriting your prompt each time.