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Get Character Sheet for Prompting

lab_get_character_sheet
Read-only

The character sheet PREPARED FOR PROMPTING: base prefix, the state sheets by name, which reference views exist, and the ready-made sentence to paste into a lab_generate prompt. Same source as lab_get_character, different shape — use this one when you are about to generate, and lab_get_character when you want the raw record. State sheets exist against character drift: one sheet per costume, light or physical-condition change, so a person stays the same person when the situation changes. THIS IS THE ANSWER to 'same person, different light': character_refs alone routes to i2i and drags the reference's lighting along, so a drastically different light fights the reference. A state sheet puts the change into the TEXT, where the prompt can win. Free.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stateNoRender the snippet for THIS state key (from `states[].key`) instead of the base state
previewNoAttach the reference views themselves, downscaled to 512px. `usable_as_character_ref` only says a URL exists — the picture says whether it is the right person, in the right view. That check is the whole point of a character sheet.
character_idYesCharacter sheet id (from lab_list_characters) — positive integer as string

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / additionalProperties
      Added value: +false
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true, so safety is covered; the description adds value beyond that by disclosing the return payload shape, stating the call is 'Free', and explaining the behavioral mechanism (a state sheet puts the change into the text so the prompt can win rather than dragging the reference's lighting). It stops short of describing pagination or error behavior, but for a read-only lookup this is solidly above the bar.

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 return contents before rationale and alternative, which is the right order. It is longer than strictly needed — the character-drift rationale and the 'THIS IS THE ANSWER' framing run a bit long — but each sentence carries usable routing or conceptual information rather than filler.

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 carries the full burden of explaining returns and does so completely (base prefix, named state sheets, reference views, paste-ready sentence). Combined with the explicit sibling routing and cost note, an agent has everything needed to select and call it correctly.

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 all three parameters are already documented in the schema, including the `states[].key` origin of `state` and the downscale/verification purpose of `preview`. The description reinforces the concept of a state but adds no syntax or format detail beyond the schema, so the baseline 3 applies.

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 (retrieve the character sheet prepared for prompting) and enumerates exactly what it returns: base prefix, state sheets by name, reference views, and a ready-to-paste sentence. It explicitly names and contrasts with the sibling lab_get_character, so an agent can distinguish the two 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 routing rule: 'use this one when you are about to generate, and lab_get_character when you want the raw record.' It further explains the scenario it is designed for ('same person, different light') and why character_refs alone fails there, which is a concrete when-to-use condition.

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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