Prompts

MCP prompts are reusable templates that fetch workspace data and return a filled message ready for LLM completion. The LLM call itself is performed by the client — prompts only prepare the input.

summarize-page

Summarize a page or database row.

Arguments

Argument	Type	Required	Default	Description
`page_id`	string	✓		Workspace item ID or database row ID
`style`	`"bullet"` \| `"paragraph"` \| `"tldr"`		`"paragraph"`	Summary style

Styles

bullet — key points as a bullet list
paragraph — concise prose summary
tldr — single-sentence summary

weekly-status-report

Generate a weekly status report from a task database.

Arguments

Argument	Type	Required	Default	Description
`database_id`	string	✓		Database to generate the report from
`period`	string		`"last week"`	Reporting period, e.g. `"this sprint"`

The prompt groups items by status (Done / In Progress / Blocked / Backlog), highlights blockers, and surfaces key wins.

kanban-triage

Review a kanban board and identify blockers, priorities, and next actions.

Arguments

Argument	Type	Required	Description
`database_id`	string	✓	Database ID of the kanban board

The prompt returns:

Items needing immediate attention
Blockers and their reasons
Items that can be deprioritized
The top 3 next actions

extract-tasks

Extract all actionable tasks from a page.

Arguments

Argument	Type	Required	Description
`page_id`	string	✓	Workspace item ID or database row ID

Returns a markdown checklist. For each task: action, owner (if mentioned), deadline (if mentioned), and priority (if indicated).

search-and-create

Search for similar existing pages and get content suggestions for a new page to avoid duplication.

Arguments

Argument	Type	Required	Description
`title`	string	✓	Title of the page you want to create
`query`	string	✓	Search query to find similar existing content

The prompt returns a markdown outline for the new page that complements (rather than duplicates) the existing content found by query.

save-memory

Persist a durable memory — a decision, preference, gotcha, or fact — into your Agent Memory database as a structured, human-readable record. Pairs with recall-context to give a long-running agent a workspace-backed memory.

Arguments

Argument	Type	Required	Default	Description
`content`	string	✓		The thing to remember, in plain language
`memory_type`	`"decision"` \| `"preference"` \| `"gotcha"` \| `"fact"`		`"fact"`	Kind of memory
`tags`	string			Comma-separated tags, e.g. `"architecture, api"`
`database_id`	string			Target memory database ID. Omit to auto-locate an Agent Memory database

The prompt resolves the target database (or, if none exists, returns instructions to create one from the Agent Memory template shape) and hands back a filled instruction telling the agent exactly what structured row to write with create_page: a concise summary title, the Type/Tags/Date properties, and the full memory as the body. The prompt only prepares the instruction — the agent performs the write with the write tools.

Tip — start from the built-in Agent Memory template (New item → Templates) so the Type / Tags / Date columns already exist. See Agent Memory.

recall-context

Recall everything the workspace already knows about a topic in one compact package: the top matching pages, each collapsed to a token-cheap outline, plus the link-graph neighborhood of the best match.

Arguments

Argument	Type	Required	Default	Description
`topic`	string	✓		What to recall context about
`limit`	number		`6`	Maximum pages to include (1–12)

The prompt runs search_workspace, collapses each hit to a heading-and-first-line outline (the same collapse get_page's outline mode uses), and appends the parent / children / outgoing links / backlinks of the top hit from the link graph. The result loads prior context in a single message instead of many search_workspace + get_page round-trips — fetch a full body with get_page only when an outline shows you need the detail.