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bulk_modify

Modify all tasks matching a filter expression in one call. Use dry run to preview changes, then confirm with a token.

Instructions

Modify all tasks matching a filter expression in a single call.

Applies the same field changes to every matching task. NON-ATOMIC: tasks are modified sequentially; a failure on one task does not stop the batch. High-impact changes require a confirm_token — call with dry_run=true first to receive the token, then resubmit with confirm_token set.

filters: Taskwarrior filter syntax (required, e.g. 'project:work +urgent'). fields: field/value pairs — same format as modify_task. For tags: {"tags_add": ["label"]} or {"tags_remove": ["label"]}.

Use modify_task for targeted single-task changes. Use dry_run=true to preview affected task count before committing. Requires MANAGER.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filtersYes
fieldsYes
dry_runNo
confirm_tokenNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations provided, the description fully discloses behavioral traits: non-atomic sequential modification, failure handling (single task failure doesn't stop batch), dry_run preview workflow, and confirm_token requirement for high-impact changes.

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?

Description is well-structured, front-loaded with purpose, and uses efficient sentences. Every sentence adds value without redundancy.

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 no annotations and an output schema present, the description covers all necessary context: behavior, parameters, usage patterns, auth requirements, and error handling.

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

Parameters4/5

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

Schema coverage is 0%, so description compensates well. It explains filters (Taskwarrior syntax), fields (field/value pairs with special tags notation), dry_run, and confirm_token. Could be slightly more precise on field format, but adds meaningful context 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 it modifies all tasks matching a filter in a single call. It distinguishes from sibling tools like 'modify_task' by specifying batch vs single-task operation.

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?

Explicitly tells when to use this tool ('Use modify_task for targeted single-task changes'), when not, and provides workflow guidance (dry_run first, confirm_token for high-impact changes). Also mentions required role: MANAGER.

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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