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by com.thefilmradar
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Autopopulate Cast

lab_cast_autopopulate

Create character sheets for the people found in a project's material — the writing counterpart to lab_cast_gaps. Creates at most 6 per call and binds each to the project. Costs an LLM run per call. Run lab_cast_gaps first if you want to see what it would create. The sheets it creates have NO reference image: each one still needs lab_generate_character_reference before lab_generate accepts it as character_refs. Fail-open: no script or no AI key returns an EMPTY created list with a note, not an error. Each created sheet carries a casting note (world, the one deviation from the obvious face, why). herkunft: true additionally lets the casting propose the person's ORIGIN as that one deviation (skin, hair and eye tone only) — off by default, because it changes who a named character is; the note names it.

Input Schema

NameRequiredDescriptionDefault
herkunftNoAllow the origin as the casting deviation (default false). Only turn it on when the person asked for casting against type by origin.
project_idYesProject id (from lab_list_projects) — positive integer as string

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / herkunft
      Added value: +{
+  "description": "Allow the origin as the casting deviation (default false). Only turn it on when the person asked for casting against type by origin.",
+  "type": "boolean"
+}
  2. Changed1 schema field changed
    • addedInput schema / additionalProperties
      Added value: +false
  3. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations only declare write/non-destructive; the description adds the operationally critical traits: a hard cap of 6 per call, per-call LLM cost, projection onto the project, missing reference images, and fail-open semantics (empty `created` plus `note` instead of an error). It also explains the side effect of `herkunft` on named characters, which no structured field conveys.

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?

Purpose and sibling distinction are front-loaded, and each clause carries operational content. The final herkunft sentence is dense and partially overlaps the schema description, which keeps it from being maximally tight.

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?

There is no output schema, and the description compensates by naming the returned fields (`created`, `note`, and the per-sheet `casting` note with world/deviation/why) plus the fail-open return shape. For a paid, mutating, batch-limited tool, everything an agent needs before calling is present.

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 100%, so the baseline is 3, but the description adds meaning beyond the schema: why `herkunft` is off by default (it changes who a named character is), that it is limited to skin/hair/eye tone, and that the casting note names it. That rationale is genuinely additive rather than a restatement.

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 ("Create character sheets for the people found in a project's material") and immediately distinguishes itself from the sibling lab_cast_gaps as "the writing counterpart". An agent can tell the two apart 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 explicit ordering guidance ("Run lab_cast_gaps first if you want to see what it would create"), a downstream prerequisite chain (sheets have no reference image, so lab_generate_character_reference is required before lab_generate will accept them as `character_refs`), and a when-not rule for herkunft. Nothing about selection 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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