Tables

Tables are structured tabular databases scoped to your project. Use them to store, query, and manage structured data that your playbooks and AI agents can read and write programmatically.

Creating a table

Open the Creation Picker (Cmd+N) and select Table, or use the create_data_store MCP tool from an agent session.

Each table has typed columns. Data is persisted in a per-project SQLite database.

Column types

Type	Description
text	Plain text
number	Numeric values
date	Date/time values
bool	True/false
relation	Foreign key to another table

Columns also support format sub-types for richer display: email, currency, percent, rating, singleSelect, multiSelect, and rank. Select formats carry options with id, label, and color.

Natural keys

An optional natural key column enables upsert semantics — inserts update existing rows that match the key instead of creating duplicates. This is essential for playbooks that run periodically and need idempotent writes.

Views

Tables support alternate view modes:

• Table — the default spreadsheet-style view.
• Kanban — cards grouped by a singleSelect column.
• Gantt — timeline view using start and end date columns.

Create and manage views via the UI or create_data_store_view / update_data_store_view MCP tools.

Querying data

Use the run_data_query MCP tool to query a table with filters, sorting, and projections. Results are returned as JSON.

Queries can also be embedded directly into notes as live chart/table blocks — the note auto-refreshes when the underlying data changes.

MCP tools

Tool	Description
create_data_store	Create a new table
get_data_store	Get a table's schema and metadata
list_data_stores	List all tables in the project
add_data_store_column	Add a column
update_data_store_column	Modify a column's type or options
delete_data_store_column	Remove a column
insert_data_store_rows	Insert or upsert rows
update_data_store_row	Update a specific row
update_data_store_rows	Bulk-update rows — a list of per-row changes, or where+set to change every matching row
delete_data_store_row	Delete a row
run_data_query	Query with filters, sort, and projection
create_data_store_view	Create a Kanban or Gantt view

Playbook integration

Two dedicated playbook step types work with tables:

• writeToStore — upserts rows with natural-key-aware writes. Validates column IDs against the live schema.
• readFromStore — queries with column filters and limits. Returns rows as step output for downstream steps to consume.