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Draft episode script

draft_episode_script

Write an episode's scene-by-scene script: draft it from premise, or adapt the writer's own scriptText; pass exactly one (400 for both or neither). It replaces the episode's scenes and returns { episode, scenesGenerated, source, sceneCountCheck }; review and edit with save_episode before generate_episode. groundInResearch adds web research to a premise draft. The series needs at least one character, and an episode that is generating is refused. Spends Galatic.AI credits (402 when the budget is short). seriesId is the flowId of the Flow Studio flow that holds the series (from list_flows).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "seriesId": "<seriesId>",
      "episodeId": "<episodeId>",
      "context": "<context>",
      "llm_model": "<llm_model>"
    },
    "name": "draft_episode_script"
  }
}

Input Schema

NameRequiredDescriptionDefault
contextYesExplain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
premiseNoWhat happens. Send this or scriptText, not both.
seriesIdYes
episodeIdYes
llm_modelYesThe exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
scriptTextNoA finished script to adapt into scenes. Send this or premise.
conversation_idNoPass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.
groundInResearchNoPremise drafts only: ground the draft in web research (costs more).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedInput schema / properties / groundInResearch / description
      Added value: +"Premise drafts only: ground the draft in web research (costs more)."
    • addedInput schema / properties / premise / description
      Added value: +"What happens. Send this or scriptText, not both."
    • addedInput schema / properties / scriptText / description
      Added value: +"A finished script to adapt into scenes. Send this or premise."
  2. First observed

TDQS

A4.4/5.0
Behavior5/5

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

The description goes far beyond what the annotations provide by detailing critical side effects: it 'replaces' existing scenes, defines the exact structure of the return object, explains the credit spending logic ('402 when the budget is short'), and outlines when it is refused ('an episode that is generating is refused').

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense and reads more like a set of operational requirements than elegant prose. While highly functional, the concentration of instructions (budget, constraints, output format) makes it slightly less concise than a perfect 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?

Given the complexity (8 parameters, multiple constraints, and side effects), the description is extremely comprehensive. It covers execution constraints, prerequisite data (character count), and the full interaction lifecycle (draft -> review -> save -> generate).

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?

Despite the schema coverage being 75%, the description provides crucial context for key parameters. It clarifies that 'premise' and 'scriptText' are exclusive, and defines 'seriesId' as the 'flowId of the Flow Studio flow that holds the series.'

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 core function is to 'Write an episode's scene-by-scene script.' It distinguishes itself by allowing the input to be either a raw 'premise' or existing 'scriptText' for adaptation.

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?

This score is earned by providing precise operational constraints and workflow guidance: the inputs (premise/scriptText) must be mutually exclusive, the series must have at least one character, and it explicitly guides the user to use 'save_episode' before 'generate_episode'.

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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