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Get Character

lab_get_character
Read-only

Get ONE character sheet in full — every descriptive field plus the three that decide what you can do with it. prompt_prefix_en is the text block describing this person; it is what the backend prepends wherever the sheet is used. reference_images decides whether the sheet is usable at all: lab_generate REJECTS a character_refs entry without one, before any quota is spent — if it is empty, run lab_generate_character_reference first. states carries the state sheets against character drift (costume, light, physical condition); lab_get_character_sheet renders them agent-readable.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
previewNoAttach the reference images themselves, downscaled to 512px. `reference_images` decides whether the sheet is usable at all — seeing them is the check, counting them is not: a sheet can carry a URL to a picture of the wrong person.
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

A4.5/5.0
Behavior5/5

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

readOnlyHint only covers the safety profile, yet the description discloses the downstream gating behavior (reference_images must be non-empty or lab_generate rejects before spending quota) and the quarantine purpose of states. This is exactly the kind of cross-tool behavioral context annotations cannot carry.

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 scope statement, then spends each sentence on a distinct high-value field. It is dense and slightly long, but no sentence is filler and the dependency ordering is clear.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description names the three load-bearing return fields (prompt_prefix_en, reference_images, states) and their roles, which is strong for a read tool. 'Every descriptive field' remains hand-wavy about the remaining payload, so it is not fully complete.

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 baseline is 3, but the description reinforces the semantics of reference_images as a usability gate and preview as a verification aid ('seeing them is the check, counting them is not'). It adds interpretive meaning beyond the raw field docs, though character_id is left entirely to 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 and resource ('Get ONE character sheet in full') and the word 'ONE' plus the field inventory distinguishes it from lab_list_characters and lab_get_character_sheet. An agent knows exactly what single resource this returns without opening a sibling.

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?

Gives explicit conditional guidance: if reference_images is empty, run lab_generate_character_reference first, and explains why lab_generate rejects such sheets before quota is spent. It stops short of an explicit when-to-use branch versus lab_get_character_sheet, implying the division only through the states note.

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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