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LPC Character Generator — MCP

LPC Character Generator — MCP

by kyuza1
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

list_categories

Discover available LPC character item categories and see how many items each contains, so you can find clothing, body, hair, and gear options for sprite creation.

Instructions

Lists item categories (e.g. body, hair, torso/shirts) with how many items each has.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_categories"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries the full burden; it does disclose the return content ('how many items each has'), which is useful since there is no output schema. It says nothing about whether this is a pure read, permission needs, or result size, which are minor for a zero-parameter listing tool but not fully covered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with zero filler; the resource comes first and the examples and return content follow immediately.

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 annotations, no output schema, and no parameters, the description needs to convey purpose and return shape, which it does by naming categories and their item counts. An agent has enough to call it correctly, though a hint about ordering or scale is absent.

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?

The tool takes zero parameters, so the baseline is 4; there is nothing for the description to disambiguate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Lists item categories') and goes further by giving concrete examples (body, hair, torso/shirts), so an agent knows exactly what domain it covers. It does not explicitly contrast with siblings like get_item or search_items, keeping it short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied: call this to discover what categories exist before searching or fetching items. There is no explicit when-to-use statement, no exclusion, and no named alternative among the many sibling tools, so it lands at minimum-viable.

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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