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., "@Legal Contract Review AgentAnalyze this Japanese NDA for potential liability risks and suggest improvements."
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.
Legal Contract Review Agent
AI-powered Japanese legal contract review agent system built with LangGraph, RAG, MCP, and Tool Calling.
Demo
Architecture
┌─────────────┐ ┌──────────────────────────────────────────┐
│ React UI │───▶│ FastAPI Backend │
└─────────────┘ │ │
│ LangGraph Agent Workflow: │
┌─────────────┐ │ parse_contract → retrieve_knowledge │
│ Claude │ │ → analyze_risks → generate_report │
│ Desktop │───▶│ │
│ (MCP Client)│ │ Tools: search_legal_knowledge │
└─────────────┘ │ analyze_clause_risk │
│ generate_suggestion │
│ │
│ RAG: ChromaDB + OpenAI Embeddings │
└──────────────────────────────────────────┘Tech Stack
LLM: OpenAI GPT-4o
Agent Framework: LangGraph (StateGraph)
RAG: ChromaDB + text-embedding-3-small
MCP: FastMCP (Python)
Backend: FastAPI
Frontend: React + Vite + TypeScript
Deployment: Docker Compose
Quick Start
Prerequisites
Docker & Docker Compose
OpenAI API Key
Setup & Run
cd legal-contract-agent
# Create .env from template and add your OpenAI API Key
cp .env.example .env
# Edit .env: OPENAI_API_KEY=sk-your-key-here
# Build and start all services
docker compose up --buildOpen http://localhost:5173 — paste a Japanese contract and click "契約書を審査する".
To stop:
docker compose down # Stop containers
docker compose down -v # Stop and remove data volumesRun Without Docker (Alternative)
# Install Python dependencies
pip install .
# Install frontend dependencies
cd frontend && npm install && cd ..
# Terminal 1: Start backend
uvicorn backend.main:app --reload
# Terminal 2: Start frontend
cd frontend && npm run devMCP Server (for Claude Desktop)
python -m backend.mcp.serverAdd to Claude Desktop config:
{
"mcpServers": {
"legal-review": {
"command": "python",
"args": ["-m", "backend.mcp.server"],
"cwd": "/path/to/legal-contract-agent"
}
}
}Project Structure
backend/
├── main.py # FastAPI entry point
├── Dockerfile # Backend container image
├── agent/
│ ├── graph.py # LangGraph workflow
│ ├── nodes.py # Agent node functions
│ ├── state.py # Agent state definition
│ └── tools.py # LangChain tools
├── rag/
│ ├── store.py # ChromaDB vector store
│ └── loader.py # Knowledge loader
├── mcp/
│ └── server.py # MCP server
└── data/
└── legal_knowledge.json # Legal knowledge (20 entries)
frontend/
├── Dockerfile # Frontend container image
└── src/
├── App.tsx # Main UI
└── App.css # Styles
docker-compose.yml # Container orchestrationKey Design Decisions
LangGraph over simple chain: Supports conditional branching, state management, and is extensible for multi-agent collaboration
RAG: Grounds agent responses in reliable legal knowledge rather than relying solely on LLM memory
MCP: Standardized AI tool protocol enabling any client (Claude Desktop, etc.) to invoke contract review capabilities
Tool Calling: Agent autonomously decides when to invoke which tool, demonstrating autonomous decision-making