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Overview

docs/en/tools/database-data/overview.mdx

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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="/en/tools/database-data/mysqltool"> Connect to and query MySQL databases with SQL operations. </Card> <Card title="PostgreSQL Search" icon="elephant" href="/en/tools/database-data/pgsearchtool"> Search and query PostgreSQL databases efficiently. </Card> <Card title="Snowflake Search" icon="snowflake" href="/en/tools/database-data/snowflakesearchtool"> Access Snowflake data warehouse for analytics and reporting. </Card> <Card title="NL2SQL Tool" icon="language" href="/en/tools/database-data/nl2sqltool"> Convert natural language queries to SQL statements automatically. </Card> <Card title="Qdrant Vector Search" icon="vector-square" href="/en/tools/database-data/qdrantvectorsearchtool"> Search vector embeddings using Qdrant vector database. </Card> <Card title="Weaviate Vector Search" icon="network-wired" href="/en/tools/database-data/weaviatevectorsearchtool"> Perform semantic search with Weaviate vector database. </Card> <Card title="MongoDB Vector Search" icon="leaf" href="/en/tools/database-data/mongodbvectorsearchtool"> Vector similarity search on MongoDB Atlas with indexing helpers. </Card> <Card title="SingleStore Search" icon="database" href="/en/tools/database-data/singlestoresearchtool"> Safe SELECT/SHOW queries on SingleStore with pooling and validation. </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"
)