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Update a draft

update_draft
Destructive

Edits a delivered draft in place: its title, main text, hooks, calls to action, on-screen text or caption (LinkedIn and X: one option at a time). Use it to keep the creator's changes on a draft before saving; for items already in the library use update_library_item, for carousel slides edit_carousel. Returns the updated draft. Uses no credits.

Input Schema

NameRequiredDescriptionDefault
bodyNoNew main text: the script, post, email copy or video script.
ctasNoAll calls to action in order.
hooksNoAll hooks in order (email: subject lines, YouTube: the two title options).
titleNoNew title, such as "The 3 month wall".
optionNoLinkedIn and X: which option to edit, 1 by default.
captionNoReels and carousels: the post caption.
draft_idYesThe draft id from generate_content or list_library.
onscreen_hooksNoReels: on-screen text, one per hook, in the same order.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare destructiveHint=true and readOnlyHint=false, so the mutation profile is known; the description adds genuinely new facts: it returns the updated draft and consumes no credits. The one appreciable gap is whether omitted fields are preserved or cleared, which matters for a destructive in-place edit of array fields like hooks and ctas.

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?

Three sentences, front-loaded with the action and its field scope, followed by disambiguation, then cost and return behavior. No filler, no restatement of the title.

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

Completeness4/5

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

For an eight-parameter mutation tool with no output schema, the description covers scope, sibling routing, return value, and credit cost. It falls just short on merge-vs-replace semantics and on what happens to array fields (hooks, ctas) that the schema describes as full ordered lists.

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?

With 100% schema coverage the schema already defines all eight parameters, so the baseline is 3. The description nonetheless adds cross-platform context by noting the 'option' parameter applies to LinkedIn and X and that only one option is edited at a time, which helps an agent map platform to parameters rather than treating them as a flat list.

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?

States a specific verb and resource ("Edits a delivered draft in place") and enumerates exactly which fields are editable: title, main text, hooks, CTAs, on-screen text, caption. It also distinguishes itself from two named siblings (update_library_item, edit_carousel) so the agent can route without opening either schema.

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?

Gives an explicit when: "keep the creator's changes on a draft before saving." Gives explicit alternatives with conditions: library items go to update_library_item, carousel slides go to edit_carousel. Nothing is left to inference.

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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