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

# Browse Your Data

> Browse catalogs, schemas, and tables in the Data Hub sidebar, then use the built-in MCP server to create notebooks from your data.

<Badge color="purple">Public Preview</Badge>

The extension's **Data Hub** panel shows all catalogs, schemas, and tables registered in your
Wherobots Organization. Browse what's available, then use the built-in MCP server to create
notebooks that query your data, all without leaving your editor.

<Info>
  **Organization Availability:** Available to all Wherobots Organization Editions.<br />

  * **Community Edition Organizations** can create and run local notebooks against the [Tiny and Micro runtimes](/develop/runtimes).
  * **Professional or Enterprise Organizations** are required for job submission, the Wherobots MCP server, and additional compute tiers. For more information, see [Upgrade Organization](/get-started/upgrade-organization) or [Organization Editions](/get-started/organization-management/organization-editions).
</Info>

## Before you start

* The [Wherobots VS Code Extension](/develop/agentic-tools) is installed and configured.
* A Wherobots **Account** within a Professional or Enterprise Edition Organization. For more information, see [Create a Wherobots Account](/get-started/wherobots-cloud/create-account).
* Your data sources are connected to Wherobots through the [Wherobots Data Hub](/get-started/initial-storage/data-hub), or you have access to the built-in [curated datasets](/tutorials/spatial-catalog/introduction/).

## Browse the Data Hub

The Data Hub panel is in the Wherobots extension sidebar. It displays a tree of all
catalogs registered in your Organization, including any
[data sources you've connected](/get-started/initial-storage/data-hub) and
Wherobots' built-in curated datasets.

1. Open the Wherobots extension sidebar by clicking the Wherobots icon in the **Activity Bar** of VS Code or your Code OSS-based editor.
2. Expand the **Data Hub** panel.
3. Navigate the tree structure:
   * **Catalogs** are at the top level, labeled as **managed** or **foreign**.
   * Expand a catalog to see its **schemas**.
   * Expand a schema to see its **tables**.

Use the **refresh** button at the top of the Data Hub panel to see newly added catalogs or
tables after completing an integration.

<Note>
  To add new data source connections, view table metadata, or delete connections, use the
  [Wherobots Data Hub](https://cloud.wherobots.com/data-hub) in the Wherobots Cloud web console.
  For more information, see [Wherobots Data Hub](/get-started/initial-storage/data-hub).
</Note>

## Create a notebook from your data

Once you can see your data in the Data Hub, use the built-in MCP server through your editor's AI assistant
to create notebooks that query it.

<Note>
  The Wherobots MCP Server requires a [Professional or Enterprise](/get-started/organization-management/organization-editions) Organization Edition. Community Edition Organizations do not have access to the Wherobots MCP server.
</Note>

<Steps>
  <Step title="Start a workspace">
    If you don't already have a running workspace, create one by running
    `Wherobots: Create Workspace` from the Command Palette. For more information, see
    [Workspaces & Usage](/develop/vscode-extension/workspaces).
  </Step>

  <Step title="Identify the data you want to work with">
    Browse the Data Hub in the sidebar to find the catalog, schema, and table you want
    to query. Note the fully qualified name, for example, `wherobots_open_data.overture_maps_foundation.buildings_building`.
  </Step>

  <Step title="Open your AI assistant in Agent mode">
    Open your editor's AI assistant (e.g. GitHub Copilot Chat in VS Code) and select **Agent** mode.

    <Tip>
      For best results, use an advanced AI model such as the latest Claude Opus, Gemini Pro, or GPT Codex.
    </Tip>
  </Step>

  <Step title="Prompt the AI assistant to create a notebook">
    Ask the assistant to build a notebook using the data you identified. For example:

    > *Create a notebook that loads the buildings table from `wherobots_open_data.overture_maps_foundation.buildings_building` and
    > calculates the total building footprint area per city.*

    The MCP server handles catalog discovery and query execution behind the scenes.
    Allow the Wherobots MCP server when prompted.
  </Step>

  <Step title="Run the notebook">
    Open the generated `.ipynb` file and run the cells against your workspace. For detailed instructions, see
    [Run a local notebook against Wherobots compute](/develop/vscode-extension/notebooks#run-a-local-notebook-against-wherobots-compute).
  </Step>
</Steps>

## Iterate on your analysis

One of the key benefits of having the Data Hub in your editor is the ability to quickly
discover and switch between datasets as your analysis evolves. Rather than starting over
when your requirements change, you can pivot to a different dataset with a single prompt.

For example, suppose you start an analysis using the **Microsoft Buildings** dataset, but then
realize the **Overture Maps** buildings data has better coverage for your region of interest.
Instead of rewriting your notebook, you can:

1. Browse the Data Hub to find the Overture Maps tables under `wherobots_pro_data.overture_maps_foundation`.
2. Ask the AI assistant to switch:

   > *The Microsoft Buildings data doesn't have the coverage I need.
   > Switch to using the Overture Maps buildings dataset from
   > `wherobots_pro_data.overture_maps_foundation` instead, and re-run the analysis.*

The MCP server discovers the new schema and the assistant adjusts your notebook
accordingly -- updating table references, adapting column names, and re-running cells.

This iterative workflow lets you:

* **Explore alternatives**: Try different datasets for the same analysis without context-switching.
* **Refine incrementally**: Add filters, join additional tables, or change aggregations with follow-up prompts.
* **Validate quickly**: Run each iteration against live Wherobots compute to see results immediately.

## Next steps

<CardGroup cols={3}>
  <Card title="AI-Assisted Notebook Development" icon="notebook" href="/develop/vscode-extension/notebooks">
    Learn more about creating and running notebooks with AI assistance.
  </Card>

  <Card title="Workspaces & Usage" icon="server" href="/develop/vscode-extension/workspaces">
    Create, start, stop, and destroy workspaces.
  </Card>

  <Card title="Wherobots Data Hub" icon="database" href="/get-started/initial-storage/data-hub">
    Manage catalogs and connections in the Wherobots Cloud web console.
  </Card>
</CardGroup>