# LangChain Integration Guide
This guide shows how to integrate Universal Database MCP Server with LangChain.
## Overview
[LangChain](https://langchain.com/) is a popular framework for building LLM applications. It supports MCP, allowing you to use database tools in your LangChain agents.
## Prerequisites
- Python 3.9+ or Node.js 18+
- LangChain installed
- Universal Database MCP Server
- Database instance
## Installation
```bash
pip install langchain langchain-mcp
```
## Configuration
### Python Example
```python
from langchain_mcp import MCPToolkit
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_openai import ChatOpenAI
# Create MCP toolkit
toolkit = MCPToolkit(
server_command="npx",
server_args=[
"universal-db-mcp",
"--type", "mysql",
"--host", "localhost",
"--port", "3306",
"--user", "root",
"--password", "your_password",
"--database", "your_database"
]
)
# Get tools
tools = toolkit.get_tools()
# Create agent
llm = ChatOpenAI(model="gpt-4")
agent = create_tool_calling_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools)
# Run query
result = agent_executor.invoke({
"input": "What tables are in the database?"
})
print(result["output"])
```
### Node.js Example
```javascript
const { MCPToolkit } = require('langchain/tools/mcp');
const { ChatOpenAI } = require('@langchain/openai');
const { createToolCallingAgent, AgentExecutor } = require('langchain/agents');
async function main() {
const toolkit = new MCPToolkit({
serverCommand: 'npx',
serverArgs: [
'universal-db-mcp',
'--type', 'mysql',
'--host', 'localhost',
'--port', '3306',
'--user', 'root',
'--password', 'your_password',
'--database', 'your_database'
]
});
const tools = await toolkit.getTools();
const llm = new ChatOpenAI({ model: 'gpt-4' });
const agent = createToolCallingAgent({ llm, tools, prompt });
const executor = new AgentExecutor({ agent, tools });
const result = await executor.invoke({
input: 'What tables are in the database?'
});
console.log(result.output);
}
main();
```
## Available Tools
| Tool | Description |
|------|-------------|
| `execute_query` | Execute SQL queries |
| `get_schema` | Get database schema |
| `get_table_info` | Get table details |
| `clear_cache` | Clear schema cache |
## Use Cases
1. **RAG with Database**: Combine document retrieval with database queries
2. **Data Analysis Agents**: Build agents that analyze database data
3. **Chatbots**: Create chatbots that can answer questions about your data
4. **Automated Reports**: Generate reports from database queries
## Best Practices
1. Use read-only database users
2. Implement query validation
3. Add rate limiting
4. Cache schema information
## Resources
- [LangChain Documentation](https://python.langchain.com/docs/)
- [LangChain MCP Integration](https://python.langchain.com/docs/integrations/tools/mcp)
- [Universal DB MCP GitHub](https://github.com/Anarkh-Lee/universal-db-mcp)
## Support
For integration issues:
- GitHub Issues: https://github.com/Anarkh-Lee/universal-db-mcp/issues
