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ScriptGrain

Create an outline

create_outline

Generate a markdown outline for a long-form piece so the user can review the structure before drafting. Free — consumes no generation credit. Returns a generation_id with status 'outline' — the same id tracks the piece through its whole lifecycle ('outline' → 'drafted' once generate_content runs → 'edited' once save_edit stores a final version); every tool that reads the id returns the current status. Pass the id to generate_content to draft from the (optionally edited) outline. Best for article, newsletter, email, press_release and other long-form types; short social types don't need one.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
briefNoWhat to write about, max 8,000 chars (required unless template_id supplies a topic; with a template it becomes the topic line).
registerNoOverride the profile's formality register for this piece.
company_idNoWrite this FOR the user's product/company: a company profile id from list_companies. The draft is grounded in that company's facts, terminology and audience (never invented specifics). Omit company_id to use the voice's default company (set in the app under Voice Brain, when the voice has one); pass null for none.
continuityNoVoice Brain continuity. Default true once the memory knows 10+ pieces: the draft is told what the author has already established on the subject (build on it) and which prior arguments not to repeat. Pass false to write fresh, ignoring the memory.
profile_idYesA 'complete' voice profile id from list_profiles.
template_idNoStart from a saved brief template (list_brief_templates): it fills every field this call leaves empty.
content_typeNoWhat kind of piece this is (required unless template_id supplies it). article = standalone long-form article; blog_intro = ONLY the opening section of a blog post, not a full piece; newsletter = an email newsletter issue; email = a single one-to-one or marketing email; short_story = fiction; linkedin = LinkedIn post; social_post = generic short social caption; twitter_thread = X/Twitter thread; instagram_caption = Instagram caption; press_release = press announcement; product_description = catalogue blurb for ONE product; ad_copy = very short advert copy; website_landing = campaign landing page driving one action; website_home = homepage introducing the whole brand; website_product = site page for one product/feature; website_about = about-us page.
english_variantNoEnglish spelling/idiom variant. Defaults to the profile's stored variant.
extra_directionNoOne-off styling guidance for this piece (max 2,000 chars).
word_count_targetNoTarget length in words (50-15,000, lands within ±15%; each content type has its own range: long-form articles, short stories and the four website page types go to 15,000, newsletters to 5,000, short formats such as linkedin keep short ranges). Credits by length: up to 5,000 words 1 credit, up to 10,000 words 2, up to 15,000 words 3. Above 4,000 words the draft is written in the background: generate_content returns at once with status 'generating'; call get_job or get_generation until the job has succeeded (about a minute per 2,000 words). Omit for the content type's default.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Adds substantial behavior beyond the annotations: it is free ('consumes no generation credit'), it returns a generation_id with status 'outline', and the id tracks the piece through 'outline' → 'drafted' → 'edited'. This lifecycle contract is exactly the kind of context annotations cannot convey and is consistent with readOnlyHint=false.

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?

Front-loaded with the purpose, then the free-credit fact, then the id lifecycle, then the use-case guidance — a logical order with no filler sentences. It is dense and slightly long, but essentially every clause carries information, so it stops just short of a 5.

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?

With no output schema, the description compensates fully by describing the return value (generation_id + status) and its lifecycle, and it clarifies the free/first-step position in the pipeline. Nothing an agent needs to call this correctly is missing.

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 already documents all 10 parameters thoroughly; per the baseline this caps the value-add at 3. The description only references the returned id and the 'optionally edited outline' flow, adding no parameter syntax or constraints 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?

States a specific verb+resource ('Generate a markdown outline for a long-form piece') plus the scope (review structure before drafting), clearly distinguishing it from the sibling generate_content that actually drafts. An agent can tell what this produces and why without opening the 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?

Explicitly names when to use it ('Best for article, newsletter, email, press_release and other long-form types') and when not to ('short social types don't need one'), and routes forward by naming generate_content as the next step consuming the returned id.

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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