Text2SQL MCP Server
by rajacsp
README.md
# Text2SQL MCP Server - Natural Language Database Queries
[](https://opensource.org/licenses/MIT)
Convert natural language questions into SQL queries and execute them against SQLite database. Get results in table or string format.
## Overview
Text2SQL is an MCP (Model Context Protocol) server that enables natural language querying of SQLite databases. Ask questions in plain English and get results formatted as tables or strings. Perfect for data analysis, reporting, and exploration without writing SQL.
**Key Capabilities:**
- Convert natural language to SQL automatically
- Support for COUNT, SELECT, AVG, SUM operations
- Smart condition parsing (age, price, category, rating, stock)
- Multiple result formats (string for single values, table for multiple rows)
- Works seamlessly with Kiro and Cline
- 9 comprehensive unit tests included
## Quick Start (5 minutes)
```bash
# 1. Populate database with sample data
python populate_kinto_db.py
# 2. Run example client
python src/text2sql_example.py
# 3. Try questions like:
# "How many users do I have?"
# "Show me top 2 users aged above 70"
# "What is the average product price?"
```
## Setup with Kiro
```bash
./setup_mcp.sh
# Restart Kiro
```
Then ask questions naturally:
```
"How many users do I have?"
"Show me top 2 users aged above 70"
"What is the average product price?"
"Show me products in Electronics category"
```
## Setup with Cline
```bash
./setup_cline.sh
# Restart VSCode
```
Use the same natural language queries in Cline.
## Query Examples
| Question | Answer |
|----------|--------|
| "How many users do I have?" | 50 |
| "Show me top 2 users aged above 70" | [Table with 2 rows] |
| "What is the average product price?" | 125.50 |
| "Show me products in Electronics category" | [Table with products] |
| "How many orders have been placed?" | 100 |
## Database Schema
| Table | Columns |
|-------|---------|
| users | id, name, email, age, created_at |
| products | id, name, price, stock, category |
| orders | id, user_id, product_id, quantity, order_date |
| reviews | id, product_id, user_id, rating, comment, review_date |
| inventory | id, product_id, warehouse_location, quantity_available, last_updated |
## Supported Query Types
- **Count**: "How many users?" → SELECT COUNT(*) FROM users
- **Select**: "Show me users" → SELECT * FROM users
- **Conditional**: "Users aged above 70" → SELECT * FROM users WHERE age > 70
- **Limit**: "Top 5 users" → SELECT * FROM users LIMIT 5
- **Aggregate**: "Average price?" → SELECT AVG(price) FROM products
## Supported Conditions
- Age: "above 70", "over 70", "> 70", "below 30"
- Price: "above 100", "below 50"
- Category: "Electronics", "Books", etc.
- Rating: "above 4", "over 4"
- Stock: "above 100"
## MCP Tools
### query_database
Convert natural language to SQL and execute.
```json
{"question": "How many users do I have?"}
```
### get_database_schema
Get database schema information.
### execute_sql
Execute raw SQL (advanced users).
```json
{"sql": "SELECT * FROM users WHERE age > 70 LIMIT 5"}
```
## Python Usage
```python
from text2sql import Text2SQL
text2sql = Text2SQL(db_path="kinto.db")
result = text2sql.query("How many users do I have?")
if result.success:
print(f"SQL: {result.query}")
print(f"Answer: {text2sql.format_result(result)}")
```
## Installation
### Option 1: Manual installation
```bash
pip install -r requirements.txt
```
### Option 2: Using Docker
```bash
docker-compose up -d
# or
make docker-build
make docker-run
```
### Option 3: Using Makefile
```bash
make install # Install dependencies
make run # Run servers
make example # Run example client
make test # Run tests
make help # Show all commands
```
## Testing
### Quick Test
```bash
# Run the example client with pre-defined queries
python src/text2sql_example.py
```
This will show you example queries and their results, then enter interactive mode where you can ask your own questions.
### Unit Tests
```bash
# Run all unit tests (9 test cases)
make test-text2sql
```
Or directly:
```bash
python -m unittest src.test_text2sql -v
```
### Manual Testing with Kiro
1. Run setup:
```bash
./setup_mcp.sh
```
2. Restart Kiro
3. Ask questions naturally:
```
"How many users do I have?"
"Show me top 2 users aged above 70"
"What is the average product price?"
```
### Manual Testing with Cline
1. Run setup:
```bash
./setup_cline.sh
```
2. Restart VSCode
3. Use Cline with same questions
### Python Testing
```python
from text2sql import Text2SQL
text2sql = Text2SQL(db_path="kinto.db")
# Test a query
result = text2sql.query("How many users do I have?")
print(f"Success: {result.success}")
print(f"SQL: {result.query}")
print(f"Answer: {text2sql.format_result(result)}")
```
### Check Database
```bash
# Verify database exists and has data
python -c "import sqlite3; conn = sqlite3.connect('kinto.db'); print('Users:', conn.execute('SELECT COUNT(*) FROM users').fetchone()[0])"
```
## File Structure
```
src/
├── text2sql.py # Core module (400+ lines)
├── text2sql_server.py # MCP server
├── text2sql_example.py # Example client
└── test_text2sql.py # Unit tests (9 tests)
mcp_text2sql_config.json # MCP configuration
kinto.db # SQLite database
```
## How It Works
1. Parse natural language query
2. Extract conditions (age, price, category, etc.)
3. Generate SQL query
4. Execute against SQLite database
5. Format results (string or table)
## Result Formats
**String Format** (single values):
```
Answer: 50
```
**Table Format** (multiple rows):
```
id | name | email | age
───────────────────────────────────
1 | Alice | alice@example.com | 75
2 | Bob | bob@example.com | 72
```
## Troubleshooting
| Issue | Solution |
|-------|----------|
| Module not found | `pip install -r requirements.txt` |
| Database not found | `python populate_kinto_db.py` |
| Query not recognized | Try rephrasing: "Show me users aged above 70" |
| No results | Check table name and condition values |
| SQL error | Use `execute_sql` tool to debug |
## Logs
Server logs: `logs/text2sql_server.log`
## Performance
- Query parsing: < 1ms
- SQL execution: < 10ms (typical)
- Total response: < 20ms (typical)
## Features
✅ Natural language queries
✅ Automatic SQL generation
✅ Multiple result formats
✅ Smart condition parsing
✅ Aggregate functions (COUNT, AVG, SUM)
✅ Kiro/Cline integration
✅ SQLite database support
✅ 9 comprehensive unit tests
## Limitations
- Pattern-based parsing (not AI-powered)
- Supports SELECT, COUNT, AVG, SUM operations
- Complex JOINs not automatically generated
- Use `execute_sql` tool for complex queries
## Future Enhancements
- [ ] LLM-based query generation
- [ ] Support for JOIN operations
- [ ] INSERT/UPDATE/DELETE operations
- [ ] Query optimization suggestions
- [ ] Result caching
- [ ] Multi-language support
## Architecture
### System Flow
```
User Question
↓
Parse Query Type (count/select/aggregate/limit)
↓
Extract Conditions (age, price, category, etc.)
↓
Generate SQL Query
↓
Execute Against SQLite
↓
Format Results (string or table)
↓
Return Answer
```
### Component Structure
```
text2sql_server.py (MCP Server)
├── query_database tool
├── get_database_schema tool
└── execute_sql tool
↓
text2sql.py (Core Module)
├── Query Parsing
├── Condition Extraction
├── SQL Generation
└── Result Formatting
↓
SQLite Database (kinto.db)
├── users (50 rows)
├── products (30 rows)
├── orders (100 rows)
├── reviews (75 rows)
└── inventory (30 rows)
```
## Detailed Query Examples
### Sample Questions
```
1. How many users do I have?
2. Show me top 5 products with price above 100
3. What is the average rating for all reviews?
4. Show me users aged above 70
5. How many orders have been placed in Electronics category?
6. What is the average product price?
7. Show me all products in Electronics category
8. How many reviews have rating above 4?
9. Show me top 10 users
10. What is the total stock available?
11. Show me products with stock below 20
12. How many products are in Books category?
13. What is the average age of users?
14. Show me orders with quantity above 5
15. How many inventory records do we have?
16. Show me top 3 products with highest price
17. What is the sum of all product prices?
18. Show me users aged below 30
19. How many products have price above 200?
20. Show me reviews with rating below 3
21. What is the average order quantity?
22. Show me products in Warehouse A location
23. How many users were created this year?
24. Show me top 5 most expensive products
25. What is the total quantity available in inventory?
```
### Count Queries
```
Q: "How many users do I have?"
SQL: SELECT COUNT(*) as count FROM users
A: 50
Q: "How many products are in stock?"
SQL: SELECT COUNT(*) as count FROM products
A: 30
```
### Select Queries
```
Q: "Show me all users"
SQL: SELECT * FROM users
A: [Table with all 50 users]
Q: "Get products"
SQL: SELECT * FROM products
A: [Table with all 30 products]
```
### Conditional Queries
```
Q: "Show me users aged above 70"
SQL: SELECT * FROM users WHERE age > 70
A: [Table with users over 70]
Q: "Products with price below 50"
SQL: SELECT * FROM products WHERE price < 50
A: [Table with affordable products]
Q: "Show me products in Electronics category"
SQL: SELECT * FROM products WHERE category = 'Electronics'
A: [Table with Electronics products]
```
### Limit Queries
```
Q: "Show me top 2 users"
SQL: SELECT * FROM users LIMIT 2
A: [Table with first 2 users]
Q: "Top 5 products"
SQL: SELECT * FROM products LIMIT 5
A: [Table with first 5 products]
```
### Combined Queries
```
Q: "Show me top 2 users aged above 70"
SQL: SELECT * FROM users WHERE age > 70 LIMIT 2
A: [Table with 2 users over 70]
Q: "Top 10 products in Electronics with stock above 20"
SQL: SELECT * FROM products WHERE category = 'Electronics' AND stock > 20 LIMIT 10
A: [Table with matching products]
```
### Aggregate Queries
```
Q: "What is the average product price?"
SQL: SELECT AVG(price) as result FROM products
A: 125.50
Q: "What is the total stock?"
SQL: SELECT SUM(stock) as result FROM products
A: 5000
Q: "Average user age?"
SQL: SELECT AVG(age) as result FROM users
A: 52.3
```
## Advanced Usage
### Using with Python
```python
from text2sql import Text2SQL
# Initialize
text2sql = Text2SQL(db_path="kinto.db")
# Simple query
result = text2sql.query("How many users do I have?")
# Check result
if result.success:
print(f"Generated SQL: {result.query}")
print(f"Raw Data: {result.data}")
print(f"Formatted: {text2sql.format_result(result)}")
else:
print(f"Error: {result.message}")
# Access result data directly
for row in result.data:
print(row)
```
### Using with MCP Client
```python
import json
from mcp.client import Client
client = Client()
# Query database
response = client.use_tool(
"text2sql-mcp-server",
"query_database",
{"question": "How many users do I have?"}
)
# Get schema
schema = client.use_tool(
"text2sql-mcp-server",
"get_database_schema",
{}
)
# Execute raw SQL
result = client.use_tool(
"text2sql-mcp-server",
"execute_sql",
{"sql": "SELECT * FROM users WHERE age > 70 LIMIT 5"}
)
```
### Custom Database
To use with your own database:
```python
from text2sql import Text2SQL
# Point to your database
text2sql = Text2SQL(db_path="path/to/your/database.db")
# Query as normal
result = text2sql.query("Your question here")
```
## Configuration
### MCP Server Configuration
The MCP server is configured in `mcp_text2sql_config.json`:
```json
{
"mcpServers": {
"text2sql-mcp-server": {
"command": "python",
"args": ["src/text2sql_server.py"],
"env": {
"PYTHONUNBUFFERED": "1"
},
"disabled": false,
"autoApprove": [
"query_database",
"get_database_schema",
"execute_sql"
]
}
}
}
```
### Database Configuration
Default database: `kinto.db`
To use a different database, modify `src/text2sql_server.py`:
```python
# Change this line:
text2sql = Text2SQL(db_path="kinto.db")
# To:
text2sql = Text2SQL(db_path="path/to/your/database.db")
```
### Logging Configuration
Logs are written to `logs/text2sql_server.log`
To change log level in `src/text2sql_server.py`:
```python
logging.basicConfig(
level=logging.DEBUG, # Change to DEBUG for verbose logging
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
handlers=[
logging.FileHandler('logs/text2sql_server.log'),
logging.StreamHandler()
]
)
```
## Query Pattern Reference
### Query Type Detection
| Pattern | Type | Example |
|---------|------|---------|
| "How many", "Count", "Total" | Count | "How many users?" |
| "Show", "Get", "List", "Find" | Select | "Show me users" |
| "Average", "Avg", "Sum" | Aggregate | "Average price?" |
| "Top", "First", "Limit" | Limit | "Top 5 users" |
| "Aged", "Priced", "Category" | Conditional | "Users aged above 70" |
### Condition Patterns
| Condition | Patterns | Example |
|-----------|----------|---------|
| Age | above, over, >, below, under, < | "aged above 70" |
| Price | above, over, >, below, under, < | "price below 50" |
| Category | category name | "Electronics category" |
| Rating | above, over, > | "rating above 4" |
| Stock | above, over, > | "stock above 100" |
## Real-World Scenarios
### Scenario 1: Customer Analysis
```
Q: "How many customers are over 70 years old?"
A: 8
Q: "Show me top 5 oldest customers"
A: [Table with oldest customers]
Q: "What is the average age of our customers?"
A: 52.3
```
### Scenario 2: Product Management
```
Q: "How many products do we have?"
A: 30
Q: "What is the average product price?"
A: 125.50
Q: "Show me products in Electronics category"
A: [Table with Electronics products]
Q: "How many products have stock above 50?"
A: 15
```
### Scenario 3: Order Analysis
```
Q: "How many orders have been placed?"
A: 100
Q: "What is the average order quantity?"
A: 4.5
Q: "Show me top 10 orders"
A: [Table with top 10 orders]
```
### Scenario 4: Inventory Management
```
Q: "What is the total quantity available?"
A: 8500
Q: "Show me inventory in Warehouse A"
A: [Table with Warehouse A inventory]
Q: "How many inventory records do we have?"
A: 30
```
## Troubleshooting Guide
### Issue: "Module not found" error
**Cause:** Dependencies not installed
**Solution:**
```bash
pip install -r requirements.txt
```
### Issue: "Database file not found"
**Cause:** kinto.db doesn't exist
**Solution:**
```bash
python populate_kinto_db.py
```
### Issue: Query not recognized
**Cause:** Query phrasing doesn't match patterns
**Solution:** Try rephrasing:
- ❌ "Give me users over 70"
- ✅ "Show me users aged above 70"
### Issue: No results found
**Cause:** Condition values don't match data
**Solution:**
1. Check table name is correct
2. Verify condition values exist in database
3. Try simpler query first
### Issue: SQL execution error
**Cause:** Generated SQL is invalid
**Solution:**
1. Check the generated SQL query in output
2. Use `execute_sql` tool to debug
3. Verify database file exists and is readable
### Issue: Kiro doesn't recognize Text2SQL server
**Cause:** MCP configuration not loaded
**Solution:**
1. Verify `mcp_text2sql_config.json` exists
2. Check MCP configuration in Kiro settings
3. Restart Kiro
4. Check logs: `logs/text2sql_server.log`
### Issue: Unexpected query results
**Cause:** Query parsed differently than expected
**Solution:**
1. Check the generated SQL query
2. Use `execute_sql` tool to test SQL directly
3. Verify database schema with `get_database_schema`
## Performance Characteristics
### Query Execution Timeline
| Step | Time |
|------|------|
| Parse query | < 1ms |
| Extract conditions | < 1ms |
| Generate SQL | < 1ms |
| Execute SQL | < 10ms (typical) |
| Format results | < 5ms |
| **Total** | **< 20ms (typical)** |
### Scalability
- Supports databases with thousands of rows
- Efficient pattern-based parsing
- No external API calls required
- Minimal memory footprint
## Security Considerations
### Input Validation
- Query pattern matching prevents invalid SQL
- Parameterized queries prevent SQL injection
- Safe error messages don't expose database details
### Database Access
- Read-only operations by default
- Optional raw SQL execution with `execute_sql` tool
- Audit trail via logging
### Best Practices
1. Use `query_database` for user-facing queries
2. Use `execute_sql` only for trusted queries
3. Monitor logs for suspicious activity
4. Restrict database file permissions
## Development
### Project Structure
```
Text2SQL/
├── src/
│ ├── __init__.py
│ ├── text2sql.py # Core module
│ ├── text2sql_server.py # MCP server
│ ├── text2sql_example.py # Example client
│ └── test_text2sql.py # Unit tests
├── logs/
│ └── text2sql_server.log # Server logs
├── kinto.db # SQLite database
├── mcp_text2sql_config.json # MCP config
├── populate_kinto_db.py # Database setup
├── README.md # This file
├── Makefile # Build commands
├── requirements.txt # Dependencies
└── setup.py # Package setup
```
### Running Tests
```bash
# Run all tests
make test
# Run specific test
python -m unittest src.test_text2sql.TestText2SQL.test_count_query -v
# Run with coverage
python -m coverage run -m unittest src.test_text2sql
python -m coverage report
```
### Test Coverage
The project includes 9 comprehensive unit tests:
1. `test_count_query` - Count queries
2. `test_select_query` - Select queries
3. `test_age_condition` - Age condition parsing
4. `test_category_condition` - Category condition parsing
5. `test_limit_query` - Limit queries
6. `test_average_query` - Average queries
7. `test_format_result_string` - String formatting
8. `test_format_result_table` - Table formatting
9. `test_invalid_query` - Error handling
### Adding New Query Types
To add support for new query types:
1. Add pattern detection in `_parse_query()` method
2. Create parsing method (e.g., `_parse_custom_query()`)
3. Add condition extraction if needed
4. Add unit tests
5. Update documentation
Example:
```python
def _parse_custom_query(self, query: str) -> Optional[str]:
"""Parse custom query type."""
# Your logic here
return sql_query
```
### Extending Condition Support
To add new condition types:
1. Add pattern matching in `_extract_conditions()`
2. Build WHERE clause condition
3. Add unit tests
4. Update documentation
Example:
```python
# In _extract_conditions()
if 'custom_field' in query:
match = re.search(r'custom_field\s+(?:above|over|>)\s+(\d+)', query)
if match:
conditions.append(f"custom_field > {match.group(1)}")
```
## API Reference
### Text2SQL Class
```python
class Text2SQL:
def __init__(self, db_path: str = "kinto.db")
def query(self, natural_language: str) -> QueryResult
def format_result(self, result: QueryResult) -> str
```
### QueryResult Dataclass
```python
@dataclass
class QueryResult:
success: bool # Query executed successfully
query: str # Generated SQL query
data: List[Dict] # Query results
message: str # Status message
format_type: str # 'table' or 'string'
```
### MCP Tools
#### query_database
Convert natural language to SQL and execute.
**Input:**
```json
{
"question": "How many users do I have?"
}
```
**Output:**
```
Query: SELECT COUNT(*) as count FROM users
Result:
50
```
#### get_database_schema
Get database schema information.
**Input:** (empty)
**Output:**
```
Database Schema:
Table: users
Columns: id, name, email, age, created_at
Table: products
Columns: id, name, price, stock, category
...
```
#### execute_sql
Execute raw SQL query.
**Input:**
```json
{
"sql": "SELECT * FROM users WHERE age > 70 LIMIT 5"
}
```
**Output:**
```
SQL: SELECT * FROM users WHERE age > 70 LIMIT 5
Result:
id | name | email | age
───────────────────────────────────
1 | Alice | alice@example.com | 75
2 | Bob | bob@example.com | 72
```
## Deployment
### Docker Deployment
```bash
# Build image
make docker-build
# Run container
make docker-run
# View logs
make docker-logs
# Stop container
make docker-stop
```
### Local Deployment
```bash
# Install dependencies
make install
# Run example
make example
# Run tests
make test
```
### Production Considerations
1. Use environment variables for database path
2. Enable logging for audit trail
3. Restrict database file permissions
4. Use read-only database user if possible
5. Monitor server logs regularly
6. Set up automated backups
## Limitations & Future Work
### Current Limitations
- Pattern-based parsing (not AI-powered)
- Supports SELECT, COUNT, AVG, SUM operations only
- Complex JOINs not automatically generated
- No support for subqueries
- No support for GROUP BY, ORDER BY
- No support for INSERT/UPDATE/DELETE
### Future Enhancements
- [ ] LLM-based query generation for complex queries
- [ ] Support for JOIN operations
- [ ] Support for GROUP BY and ORDER BY
- [ ] INSERT/UPDATE/DELETE operations
- [ ] Query optimization suggestions
- [ ] Result caching for repeated queries
- [ ] Multi-language support
- [ ] Query history and analytics
- [ ] Custom function support
- [ ] Parameterized query templates
## Contributing
To contribute to this project:
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Update documentation
6. Submit a pull request
## Support & Documentation
- **Quick Start:** See "Quick Start" section above
- **Examples:** See "Query Examples" section
- **Troubleshooting:** See "Troubleshooting Guide" section
- **API Reference:** See "API Reference" section
- **Logs:** Check `logs/text2sql_server.log`
## FAQ
**Q: Can I use this with my own database?**
A: Yes! Just point to your database file: `Text2SQL(db_path="your_db.db")`
**Q: Does it support complex queries?**
A: For complex queries, use the `execute_sql` tool to run raw SQL directly.
**Q: Is it secure?**
A: Yes, it uses parameterized queries and pattern-based parsing to prevent SQL injection.
**Q: Can I modify the database?**
A: By default, only SELECT operations are supported. Use `execute_sql` for other operations.
**Q: How do I add new query types?**
A: See "Adding New Query Types" in the Development section.
**Q: What databases are supported?**
A: Currently SQLite. Other databases can be added by modifying the database connection code.
**Q: Can I use this in production?**
A: Yes, with proper configuration and monitoring. See "Production Considerations" section.
**Q: How do I report bugs?**
A: Check the logs in `logs/text2sql_server.log` and review the Troubleshooting Guide.
### Screenshots
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
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