> ## Documentation Index
> Fetch the complete documentation index at: https://docs.markifact.com/llms.txt
> Use this file to discover all available pages before exploring further.

# List Tables

> Retrieve all tables in a BigQuery project or dataset to explore available data and plan your workflows.

**List Tables** returns all tables within a BigQuery project or specific dataset. Essential for discovering available data, planning data workflows, and managing your BigQuery table inventory.

***

## When to Use It

* Discover available tables before building data workflows
* Find specific tables for dynamic workflow construction
* Use it as AI Tool for AI Agents to understand your data structure
* Audit table usage and organization across projects

***

## Inputs

| Field       | Type   | Required | Description                                                          |
| ----------- | ------ | -------- | -------------------------------------------------------------------- |
| **Project** | Select | Yes      | Google BigQuery project to search in                                 |
| **Dataset** | Select | No       | Specific dataset to list tables from (optional - lists all if empty) |
| **Limit**   | Number | No       | Maximum number of tables to return (1-1000)                          |

***

## Outputs

| Output          | Description                                        |
| --------------- | -------------------------------------------------- |
| **Tables List** | List of all tables with names, types, and metadata |

***

## Credit Cost

1 credit per operation.

***

## Real-World Examples

**BigQuery AI Agent:**

```
AI Agent → List Datasets → List Tables → Run Query
"Allow AI to discover all available datasets and tables, then run SQL queries"
```

**Data Discovery for New Projects:**

```
List Tables → AI Analyze Data → Write to Sheets
"Explore available marketing data tables before building analytics workflow"
```

***

## Understanding Table Information

The returned data includes:

**Table Metadata:**

* Table ID and full name
* Table type (TABLE, VIEW, EXTERNAL)
* Creation and modification timestamps
* Dataset and project information
* Row count and size information (when available)

**Table Types:**

* **Standard Tables:** Regular BigQuery tables with stored data
* **Views:** Virtual tables based on SQL queries
* **External Tables:** Tables that reference data outside BigQuery

***

## FAQ

<Accordion title="What's the difference between listing with and without selecting a dataset?">
  Without a dataset, you get all tables across all datasets in the project. With a dataset selected, you get only tables from that specific dataset. Use dataset filtering for focused exploration.
</Accordion>

<Accordion title="What information do I get about each table?">
  You'll receive table name, type (table/view/external), creation date, modification date, project and dataset info, and basic metadata like row counts when available.
</Accordion>

<Accordion title="Can I see tables I don't have permission to access?">
  No, you'll only see tables that your connected Google account has permission to view. Restricted tables won't appear in the results.
</Accordion>

<Accordion title="How do I find tables with specific characteristics?">
  Use the List Tables results with other workflow nodes like Conditional Split or AI Analyze Data to filter by table name patterns, creation dates, or other metadata.
</Accordion>

<Accordion title="What if I have hundreds of tables and hit the limit?">
  Increase the limit parameter up to 1000, use dataset filtering to narrow results, or run multiple queries. Consider if you need all tables for your specific use case.
</Accordion>

<Accordion title="How does this help with data governance?">
  Regular table inventory helps track data growth, identify unused tables, ensure proper naming conventions, and maintain organized data architecture for compliance.
</Accordion>

<Accordion title="Can I use this to identify the largest or most active tables?">
  The metadata includes basic size and activity information. Combine results with AI Analyze Data to identify patterns or use Conditional Split to filter by specific criteria.
</Accordion>