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Start a story series

create_story

Start a story series: one AI influencer (a completed, unlocked one from list_characters) living a continuing life in short talking episodes with its own voice, the plot carrying over between episodes. Needs a title and a premise (who the influencer is, the recurring settings, the built-in tension); tone is optional (e.g. "dry humour, cliffhanger endings"). Free to create; episodes are charged only when approved. Stories are a plan feature: the tool says so when the user's plan lacks them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toneNoDelivery and mood, optional.
titleYes
premiseYesThe influencer's life in two or three sentences: who, where, what keeps happening.
characterYesThe influencer id (completed and unlocked).
resolutionNo480P (default, cheaper per scene) or 768P.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
nextYes
storyYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly=false and destructive=false, so the safety profile is covered. The description adds substantial context beyond them: creation is free, episodes are charged only when approved, and the tool self-reports when the user's plan lacks stories. It doesn't disclose return values, but an output schema exists.

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-loads the action and what a series is, then folds in constraints, pricing, and plan gating. It is dense and slightly packed, but every sentence carries real information; nothing is redundant.

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 an output schema handling return values, the description covers everything an agent needs: required inputs, the character constraint, billing model, plan gating, and how episodes continue the plot. Nothing material is missing for a creation tool.

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 80% (baseline 3), and the description still adds meaning: 'character' must be completed and unlocked from list_characters, 'premise' is framed as who/where/what keeps happening, and 'tone' gets a concrete example. 'resolution' is left entirely to the schema, which is the only gap.

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 ('Start a story series') and immediately defines what a series is: one AI influencer living a continuing life in short talking episodes with a carrying-over plot. This clearly distinguishes it from siblings like create_character, draft_episode, and approve_episode.

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?

Names the source of the required character ('a completed, unlocked one from list_characters'), which routes the agent to the right sibling first, and explains the approval/charging workflow and plan gating. It lacks an explicit when-not-to-use statement versus draft_episode, but the entry-point context is clear.

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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