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expand_all

Break down pending tasks into detailed subtasks automatically, using complexity, defaults, or research-based methods for precise task management in AI-driven development.

Instructions

Expand all pending tasks into subtasks based on complexity or defaults

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fileNoAbsolute path to the tasks file in the /tasks folder inside the project root (default: tasks/tasks.json)
forceNoForce regeneration of subtasks for tasks that already have them
numNoTarget number of subtasks per task (uses complexity/defaults otherwise)
projectRootNoAbsolute path to the project root directory (derived from session if possible)
promptNoAdditional context to guide subtask generation for all tasks
researchNoEnable research-backed subtask generation (e.g., using Perplexity)
tagNoTag context to operate on
Behavior2/5

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

With no annotations, the description carries full burden but provides minimal behavioral insight. It mentions 'expand' (implies mutation) and 'force regeneration' (hints at idempotency), but omits critical details like whether this is destructive to existing subtasks, requires specific permissions, or has rate limits.

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?

Single sentence, front-loaded with core action ('expand all pending tasks'), zero waste. Efficiently conveys the essence without redundancy.

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

Completeness2/5

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

For a mutation tool with 7 parameters, no annotations, and no output schema, the description is inadequate. It lacks behavioral context (e.g., side effects, error handling), output expectations, and fails to compensate for the missing structured data, leaving significant gaps for agent usage.

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 description coverage is 100%, so the schema fully documents all 7 parameters. The description adds no parameter-specific semantics beyond implying 'complexity or defaults' relates to the 'num' parameter, but this is already covered in the schema. Baseline 3 is appropriate.

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

Purpose4/5

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

The description clearly states the verb ('expand') and resource ('pending tasks'), specifying they become subtasks based on complexity or defaults. It distinguishes from siblings like 'expand_task' (singular) and 'add_subtask' (manual addition), but doesn't explicitly contrast them.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives like 'expand_task' (for single tasks) or 'add_subtask' (manual). The description implies batch processing of 'all pending tasks,' but lacks context on prerequisites or exclusions.

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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