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spawn_and_continue

Split your work by spawning subtasks and a continuation that resumes after they complete, with automatic dependency rewiring in your DAG.

Instructions

Split your work: spawn subtasks and a continuation that resumes after they complete.

Use when you discover you need additional work done before you can finish. The DAG rewires automatically — your downstream dependents will wait for the continuation, not your partial result.

Args: step_id: Your step ID. partial_output: What you've done so far. subtasks: List of {action, agent?} dicts for work that needs doing. continuation: Action description for the step that resumes your work after subtasks complete.

Returns: IDs of created subtasks and the continuation step.

Note: This tool is hidden at max depth — you must complete atomically.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
step_idYes
partial_outputYes
subtasksYes
continuationYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations provided, so the description carries full burden. It explains automatic DAG rewiring and that downstream dependents wait for the continuation. Also notes the tool is hidden at max depth. These are important behavioral traits beyond the schema.

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

Conciseness4/5

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

The description is moderately lengthy but well-structured with Args, Returns, and a Note. It is front-loaded with the main purpose and each sentence contributes useful information.

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?

Given the tool's complexity and lack of annotations, the description covers purpose, usage, behavior, parameters, and return values. It also mentions the hidden constraint. An output schema exists, so return explanation is sufficient.

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

Parameters5/5

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

Schema description coverage is 0%, but the description adds clear meanings for each parameter: step_id, partial_output, subtasks (structuring as dicts), and continuation. This adds significant value over the bare 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 'Split your work: spawn subtasks and a continuation' with a specific verb and resource. It distinguishes from siblings like `complete_step` or `fail_step` by focusing on task decomposition.

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

Usage Guidelines4/5

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

The description says 'Use when you discover you need additional work done before you can finish' which is explicit usage guidance. It does not mention when not to use or alternatives, but the context with siblings implies differentiation.

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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