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kyuza1

LPC Character Generator — MCP

by kyuza1
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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random_character

Roll a random animated character with skin, hair, clothes and shoes, plus optional beard or hat. Returns items, body type and URL to review and pass to generate_character.

Instructions

Rolls a random character (skin, head, hair, clothes, shoes and sometimes a beard, hat or vest), using only items that have every animation. Does not render: returns {items, body_type, url} to review and pass to generate_character. fixed_items are always added (e.g. a weapon). Use seed to repeat the same roll. licenses limits the roll to those license families (e.g. ["CC0", "OGA-BY"]).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNo
licensesNo
body_typeNomale
fixed_itemsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.5.0
    • addedInput schema / properties / licenses
      Added value: +{
+  "anyOf": [
+    {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Licenses"
+}
  2. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses that no rendering occurs, that returned items are filtered to those with complete animations, that fixed_items are unconditionally merged in, and that seed makes rolls reproducible. Only permissions/rate/error behavior is absent, which is a minor gap for a pure randomizer.

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?

Dense but front-loaded: the core action and the no-render contract come first, then parameter notes. Every sentence carries information, though the parenthetical slot list is longer than strictly necessary.

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?

There is no output schema, yet the description spells out the return shape ({items, body_type, url}), which is exactly what an agent needs to chain into generate_character. Combined with the animation-completeness and fixed_items behavior, it is nearly complete; only body_type's semantics are missing.

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 coverage is 0%, so the description must compensate. It explains fixed_items (always added, e.g. a weapon), seed (repeat the same roll), and licenses (restricts to named license families, with an example). However, body_type — a meaningful selector defaulting to 'male' — is never mentioned, leaving one of four parameters undocumented anywhere.

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 (rolls) and resource (random character) and enumerates the randomized slots (skin, head, hair, clothes, shoes, plus conditional beard/hat/vest). It also names the sibling it hands off to (generate_character), so the agent can distinguish it from generate_character and preview_character without opening a schema.

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?

Explicitly frames this as the non-rendering step whose output is meant to be reviewed and passed to generate_character, which gives clear context for when to pick it. It does not state when-not-to-use it or contrast directly against preview_character/generate_batch, so it falls short of a 5.

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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