Natural Language Database Query System Using MCP
implementing MCP servers to facilitate secure connections and data access across these distributed sytem enabling seamless querying across databases. The goal is to democratize data access so business users can get insights on their own, even when those insights require data from both databases.
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Natural Language Database Query System
A Python-based system that transforms natural language questions into database queries using Model Context Protocol (MCP) servers.
Overview
This system enables Marcus's team to serve data requests by:
- Creating MCP servers for both primary and analytics databases
- Translating business questions in plain English to SQL queries
- Automatically discovering and navigating complex data relationships
- Presenting results in a user-friendly format for non-technical users
- Handling errors gracefully and providing meaningful feedback
Architecture
- MCP Servers: Handle secure connections and queries to multiple databases
- Natural Language Processing: Converts English questions to SQL queries
- Schema Discovery: Automatically maps database structures and relationships
- Query Execution: Safe query execution with result formatting
- Error Handling: Comprehensive error management and validation
Installation
pip install -e .
Quick Start
from nldb_query import NLDBQuerySystem
# Initialize the system
query_system = NLDBQuerySystem()
# Ask a question in natural language
result = query_system.query("What are our top 10 customers by revenue this quarter?")
print(result.formatted_response)
Development
# Install development dependencies
pip install -e ".[dev]"
# Run tests
pytest tests/
# Run linting
black src/ tests/
flake8 src/ tests/
Configuration
See docs/configuration.md for detailed configuration options.
License
MIT License