CLIENT_HANDOFF.md•8.36 kB
# LangChain Agent MCP Server - Client Handoff Document
**Project:** LangChain Agent MCP Server
**Version:** 1.0.0
**Date:** January 2025
**Status:** ✅ Production Ready
---
## Executive Summary
This document provides a complete overview of the LangChain Agent MCP Server that has been developed and delivered. The server is a production-ready backend service that exposes advanced AI agent capabilities through the Model Context Protocol (MCP), enabling seamless integration with MCP-compliant clients.
## What Has Been Delivered
### ✅ Complete Backend Server Implementation
A fully functional FastAPI-based server that:
- **Exposes MCP-compliant endpoints** (`/mcp/manifest` and `/mcp/invoke`)
- **Integrates LangChain agents** with multi-step reasoning capabilities
- **Provides tool support** (web search, weather lookup, and extensible framework for custom tools)
- **Handles errors gracefully** with structured error responses
- **Includes comprehensive logging** for observability
- **Supports optional authentication** via API keys
- **Is containerized** with Docker for easy deployment
### ✅ Key Features Implemented
| Feature | Status | Description |
|---------|--------|-------------|
| MCP Manifest Endpoint | ✅ Complete | Returns tool definitions in MCP format |
| MCP Invoke Endpoint | ✅ Complete | Executes LangChain agent with user queries |
| LangChain Agent Integration | ✅ Complete | ReAct agent with configurable tools |
| Error Handling | ✅ Complete | Structured error responses per MCP protocol |
| Authentication | ✅ Complete | Optional API key-based authentication |
| Docker Support | ✅ Complete | Full containerization with Dockerfile |
| Logging | ✅ Complete | Comprehensive request/response logging |
| Testing Suite | ✅ Complete | Unit tests for all endpoints |
### ✅ Technical Stack
- **Framework:** FastAPI 0.121.3
- **Agent Framework:** LangChain 0.3.25, LangGraph 1.0.1
- **LLM Integration:** LangChain OpenAI 0.3.35
- **Language:** Python 3.11+ (tested on Python 3.13)
- **Deployment:** Docker-ready
- **Testing:** Pytest
## Project Structure
```
.
├── src/
│ ├── main.py # FastAPI application with MCP endpoints
│ ├── agent.py # LangChain agent definition and tools
│ └── mcp_manifest.json # MCP manifest configuration
├── tests/
│ └── test_mcp_endpoints.py # Comprehensive test suite
├── Dockerfile # Container configuration
├── requirements.txt # Python dependencies
├── run_server.py # Helper script for easy startup
├── README_BACKEND.md # Complete technical documentation
└── .env # Environment configuration (create this)
```
## Quick Start Guide
### Prerequisites
- Python 3.11 or higher
- OpenAI API key
- (Optional) Docker for containerized deployment
### Installation Steps
1. **Install Dependencies:**
```powershell
# Windows
py -m pip install -r requirements.txt
# Linux/Mac
pip install -r requirements.txt
```
2. **Configure Environment:**
Create a `.env` file in the project root:
```env
OPENAI_API_KEY=your-openai-api-key-here
OPENAI_MODEL=gpt-4o-mini
PORT=8000
HOST=0.0.0.0
```
3. **Start the Server:**
```powershell
# Windows
py run_server.py
# Linux/Mac
python run_server.py
```
4. **Verify Installation:**
- Health check: `http://localhost:8000/health`
- API docs: `http://localhost:8000/docs`
- MCP Manifest: `http://localhost:8000/mcp/manifest`
## API Endpoints
### MCP Manifest
- **Endpoint:** `GET /mcp/manifest`
- **Purpose:** Returns the MCP manifest declaring available tools
- **Response:** JSON object with tool definitions
### MCP Invoke
- **Endpoint:** `POST /mcp/invoke`
- **Purpose:** Executes the LangChain agent with a user query
- **Request Body:**
```json
{
"tool": "agent_executor",
"arguments": {
"query": "What is the weather in New York?"
}
}
```
- **Response:**
```json
{
"content": [
{
"type": "text",
"text": "Agent response..."
}
],
"isError": false
}
```
## Configuration Options
| Variable | Description | Default | Required |
|----------|-------------|---------|----------|
| `OPENAI_API_KEY` | OpenAI API key | - | ✅ Yes |
| `OPENAI_MODEL` | OpenAI model to use | `gpt-4o-mini` | No |
| `PORT` | Server port | `8000` | No |
| `HOST` | Server host | `0.0.0.0` | No |
| `API_KEY` | Optional API key for authentication | - | No |
| `VERBOSE` | Enable verbose logging | `false` | No |
| `MAX_ITERATIONS` | Maximum agent iterations | `10` | No |
| `CORS_ORIGINS` | CORS allowed origins | `*` | No |
## Docker Deployment
### Build and Run
```bash
# Build the image
docker build -t langchain-agent-mcp-server .
# Run the container
docker run -p 8000:8000 \
-e OPENAI_API_KEY=your-api-key \
-e OPENAI_MODEL=gpt-4o-mini \
langchain-agent-mcp-server
```
## Customization
### Adding New Tools
Edit `src/agent.py` and add tools to the `create_agent_tools()` function. The framework is designed for easy extension.
### Modifying Agent Behavior
- **Change LLM model:** Update `OPENAI_MODEL` in `.env`
- **Adjust reasoning depth:** Modify `MAX_ITERATIONS`
- **Custom prompts:** Edit prompt loading in `src/agent.py`
## Testing
Run the test suite:
```bash
py -m pytest tests/ -v
```
All tests are included and verify:
- Manifest endpoint functionality
- Invoke endpoint with valid/invalid requests
- Error handling
- Authentication (when enabled)
## Security Considerations
1. **API Key Protection:** Never commit `.env` file to version control
2. **Authentication:** Enable `API_KEY` for production deployments
3. **Network Security:** Deploy behind a firewall
4. **Rate Limiting:** Consider adding rate limiting for production use
## Performance Characteristics
- **Stateless Design:** Enables horizontal scaling
- **Agent Reuse:** Agent initialized once at startup, reused for all requests
- **Target Latency:** P95 under 5 seconds for standard 3-step ReAct chains
- **Scalability:** Can run multiple instances behind a load balancer
## Troubleshooting
### Server Won't Start
- Verify Python 3.11+ is installed
- Check that all dependencies are installed
- Ensure `.env` file exists with `OPENAI_API_KEY` set
### API Key Not Detected
- Verify `.env` file is in the project root
- Check that the API key is on a single line without quotes
- Restart the server after updating `.env`
### Agent Execution Errors
- Verify OpenAI API key is valid and has credits
- Check network connectivity to OpenAI API
- Review server logs for detailed error messages
## Support and Documentation
- **Technical Documentation:** See `README_BACKEND.md` for complete technical details
- **API Documentation:** Available at `http://localhost:8000/docs` when server is running
- **Product Requirements:** See `Product Requirements Document_ LangChain Agent MCP Server.md`
## Next Steps
1. **Review the codebase** - All source code is well-documented
2. **Test the endpoints** - Use the provided test suite or test manually via `/docs`
3. **Customize as needed** - Add custom tools or modify agent behavior
4. **Deploy to production** - Use Docker for consistent deployment
## Compliance with Requirements
This implementation fully satisfies all requirements from the Product Requirements Document:
- ✅ **FR1-FR6:** All functional requirements implemented
- ✅ **NFR1-NFR4:** All non-functional requirements met
- ✅ **MCP Compliance:** 100% compliant with Model Context Protocol
- ✅ **Documentation:** Comprehensive documentation provided
- ✅ **Testing:** Complete test suite included
## Questions or Issues?
For technical questions or issues:
1. Review the `README_BACKEND.md` documentation
2. Check server logs for error messages
3. Review the test suite for usage examples
4. Consult the FastAPI documentation at `/docs` endpoint
---
**Delivery Status:** ✅ Complete and Ready for Production Use
**Last Updated:** January 2025
