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storyline_edit_brief

Edit a storyline brief using AI from a natural-language instruction, with optional optimistic concurrency for safe updates.

Instructions

Apply an AI-driven edit to the current brief using a natural-language instruction. Examples: 'Make the tone more casual', 'Add a proof point about our 99% uptime SLA'. Optionally pass expectedVersion for optimistic concurrency.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
brandIdNoBrand ID (defaults to active brand)
instructionYesNatural-language edit instruction
storylineIdYesStoryline ID
expectedVersionNoOptimistic-concurrency version (omit to skip version check)
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses the AI-driven nature, mentions optimistic concurrency via expectedVersion, and implies a mutation. However, it does not specify whether the edit is incremental or a full rewrite, whether prior content is preserved, or what happens if the brief does not exist. There is no mention of return behavior or side effects.

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?

The description is two sentences: a clear statement of purpose and a note about expectedVersion, with illustrative examples. It is front-loaded with the core action and avoids extraneous detail.

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

Completeness3/5

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

Given the tool's moderate complexity (4 parameters, no output schema), the description covers the main action and an optional parameter but omits details about the result or expected behavior on failure. It adequately positions the tool among siblings but leaves some operational ambiguity.

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%, so the schema fully documents all parameters. The description adds value by giving examples for the instruction and explaining expectedVersion's purpose, but this overlaps with existing schema descriptions. It does not meaningfully improve parameter understanding beyond the 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 applies an AI-driven edit to the current brief using a natural-language instruction. Specific verb ('apply'), resource ('brief'), and mechanism ('natural-language instruction') distinguish it from siblings like storyline_set_brief or storyline_update. Examples further clarify the intended use.

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 establishes a clear use case: editing an existing brief with natural-language instructions, with examples illustrating typical edits. It does not explicitly mention when not to use it or name alternative tools, but the context is unambiguous enough to guide selection.

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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