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pyularn-mcp

pyularn-mcp

by R0x7
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_state

Returns structured player state as JSON—stats, HP, gold, dungeon level, position, inventory, effects, spells—so AI agents can inspect Ularn gameplay.

Instructions

Return structured player state as JSON: stats, HP, gold, dungeon level, position, inventory, active effects and known spells.

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": "get_state"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

No annotations exist, so the description carries the burden, and it does disclose the return payload by naming stats, HP, gold, dungeon level, position, inventory, active effects and spells. It does not explicitly state that the call is non-mutating or that no arguments are needed, which keeps it from a 5.

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?

One front-loaded sentence with the payload enumerated after the verb and format. The list is long but every item earns its place by substituting for the absent output schema.

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 must describe the return shape, and it does so at field-name granularity for a zero-argument getter. Detail such as the type of inventory entries or HP units is missing, but that is a minor gap for this complexity.

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 schema declares zero parameters and 100% coverage, so there is no parameter meaning to add. Baseline for a no-parameter tool is 4.

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?

The description uses a specific verb ("Return") and resource ("structured player state") and enumerates the contents, so an agent knows exactly what it gets. The contrast with the sibling get_screen is implied by "structured ... as JSON" but never stated, so it stops 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 only implied: the phrasing suggests a read-only snapshot to call when structured data is needed, but there is no explicit when-to-use rule or naming of alternatives such as get_screen for visual output. An agent can infer the case but must do the work itself.

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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