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 → analyze_risks │
│ Claude │ │ → generate_report │
│ Desktop │───▶│ │
│ (MCP Client)│ │ Tools: analyze_clause_risk (RAG inside) │
└─────────────┘ │ generate_suggestion (LLM inside) │
│ │
│ 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
Text Splitting: langchain-text-splitters for document chunking
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"
}
}
}Key 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
TXT Chunking: Long
.txtdocuments are split byRecursiveCharacterTextSplitter(chunk_size=200, overlap=40) and stored alongside JSON knowledge. Both are retrieved uniformly bystore.search().Contracts are query-only: User contract text is never stored in the vector database — only the curated knowledge base is indexed.
RAG Evaluation
The project includes a built-in eval module to measure the retrieval quality of the RAG pipeline.
What it evaluates
The analyze_clause_risk tool relies on ChromaDB search to retrieve relevant legal knowledge for each contract clause. The eval module measures how well this retrieval performs.
Metrics
Metric | Description |
Recall@K | Fraction of relevant documents found in the top-K results |
MRR | Mean Reciprocal Rank — average of 1/rank of the first relevant result |
Dataset
5 hand-labeled evaluation samples in backend/data/eval_dataset.json, covering typical contract risk scenarios:
ID | Scenario |
eval_001 | Unlimited liability clause |
eval_002 | Excessive non-compete period (5 years) |
eval_003 | Unilateral termination right |
eval_004 | IP / copyright assignment |
eval_005 | NDA with no time limit |
Each sample contains the query text and the expected relevant document IDs from legal_knowledge.json.
Run the eval
# Start backend first
docker compose up --build backend
# Run evaluation with default k=3
curl http://localhost:8000/api/eval/rag
# Run with custom k
curl "http://localhost:8000/api/eval/rag?k=5"Example response
{
"k": 3,
"num_samples": 5,
"mean_recall_at_k": 0.72,
"mrr": 0.85,
"per_sample": [
{
"id": "eval_001",
"description": "損害賠償無制限条項",
"recall_at_k": 0.667,
"reciprocal_rank": 1.0,
"retrieved_ids": ["civil_code_415", "risk_liability_unlimited", "civil_code_416"],
"relevant_ids": ["civil_code_415", "civil_code_416", "risk_liability_unlimited"]
}
]
}This server cannot be installed
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