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---
title: "Overview"
description: "Connect to databases, vector stores, and data warehouses for comprehensive data access"
icon: "face-smile"
---
These tools enable your agents to interact with various database systems, from traditional SQL databases to modern vector stores and data warehouses.
## **Available Tools**
<CardGroup cols={2}>
<Card title="MySQL Tool" icon="database" href="/tools/database-data/mysqltool">
Connect to and query MySQL databases with SQL operations.
</Card>
<Card title="PostgreSQL Search" icon="elephant" href="/tools/database-data/pgsearchtool">
Search and query PostgreSQL databases efficiently.
</Card>
<Card title="Snowflake Search" icon="snowflake" href="/tools/database-data/snowflakesearchtool">
Access Snowflake data warehouse for analytics and reporting.
</Card>
<Card title="NL2SQL Tool" icon="language" href="/tools/database-data/nl2sqltool">
Convert natural language queries to SQL statements automatically.
</Card>
<Card title="Qdrant Vector Search" icon="vector-square" href="/tools/database-data/qdrantvectorsearchtool">
Search vector embeddings using Qdrant vector database.
</Card>
<Card title="Weaviate Vector Search" icon="network-wired" href="/tools/database-data/weaviatevectorsearchtool">
Perform semantic search with Weaviate vector database.
</Card>
</CardGroup>
## **Common Use Cases**
- **Data Analysis**: Query databases for business intelligence and reporting
- **Vector Search**: Find similar content using semantic embeddings
- **ETL Operations**: Extract, transform, and load data between systems
- **Real-time Analytics**: Access live data for decision making
```python
from crewai_tools import MySQLTool, QdrantVectorSearchTool, NL2SQLTool
# Create database tools
mysql_db = MySQLTool()
vector_search = QdrantVectorSearchTool()
nl_to_sql = NL2SQLTool()
# Add to your agent
agent = Agent(
role="Data Analyst",
tools=[mysql_db, vector_search, nl_to_sql],
goal="Extract insights from various data sources"
)