---
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"
) 