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task_extract_from_note

Split unstructured prose from notes into a list of candidate tasks with source line tracking. Preview proposed tasks in dry-run mode before creating them in a target project.

Instructions

Mechanically split prose into a candidate-task list with source-line provenance. Source can be a task's note (kind: 'task'), a project's note (kind: 'project'), or inline text (kind: 'inline') — useful for piping a transcript through capture-meeting. Two-phase contract: dryRun=true returns { proposed, unmappedLines }; dryRun=false with confirmation: ProposedTask[] creates the (possibly-edited) tasks in targetProjectId via batchCreateTasks semantics. Returns { phase: 'dryRun', proposed, unmappedLines } or { phase: 'created', outcome: BatchOutcome } accordingly. Do NOT use this tool when you already have structured tasks — call task_batch_create directly instead. Prefer this helper when the input is a wall-of-text note that needs splitting. Side effects: dryRun=true is read-only; dryRun=false creates tasks in the target project. Mutations do not sync automatically — call sync_trigger if cross-device visibility matters. Example: task_extract_from_note({ source: { kind: "task", id: "abc123" }, dryRun: true })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dryRunNoDefault true — return proposals without creating. false requires confirmation[].
sourceYesWhere to read prose from.
confirmationNoRequired when dryRun is false. The (possibly-edited) ProposedTask[] the agent has confirmed with the user.
targetProjectIdYesProject that will receive created tasks on dryRun=false. Read-only on dryRun=true.
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description fully details behavior: read-only vs create, no auto-sync, two-phase contract with return shapes.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured, front-loaded with primary action, each sentence adds unique value, includes example.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, but description provides expected return shapes, covers all use cases, and includes side effects and an example.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with clear descriptions; the tool description adds context on return values but not significant new parameter meaning beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool splits prose into candidate tasks with provenance, and explicitly distinguishes from sibling task_batch_create.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use (wall-of-text) and when-not-to-use (structured tasks, call task_batch_create instead), plus the two-phase contract.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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