geo-figure-mcp

assign_labels

Converts region names into A–E or (가)–(마) symbols for geography exam maps, supporting random, north–south, west–east, or given ordering.

Instructions

지역 이름을 A~~E 또는 (가)~~(마) 기호로 바꾼다. 순서: random(기본) / north-south / west-east / given.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "names": "<names>"
    },
    "name": "assign_labels"
  }
}

Input Schema

Name	Required	Description	Default
`seed`	No
`names`	Yes
`order`	No		random
`style`	No		AE
`coords`	No	north-south·west-east 일 때 {이름:[경도,위도]}

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Nov 9, 2026

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations, the description carries the full load, but it doesn't disclose whether output is deterministic given a seed, what happens when order is north-south/west-east without coords, whether inputs are mutated or a new mapping is returned, or whether duplicate names collide. Only the default ordering behavior is disclosed.

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?

Two compact sentences with the core action front-loaded and the ordering options enumerated. Nothing is wasted, though the telegraphic '순서:' phrasing is slightly terse for a tool with five parameters.

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

Completeness2/5

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

For a five-parameter tool with a nested coords object, no annotations, and no output schema, the description is thin. It leaves the agent guessing about seed semantics, the coords requirement for geographic ordering, and the shape of the returned label mapping.

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 low (20%), so the description must compensate. It does explain that style selects between A~E and (가)~(마), which the schema's bare enum does not, and it restates the order options with their default. However, it says nothing about seed (presumably randomness control), the names array requirements, or that coords is required for geographic ordering.

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 says the tool renames region names into A~E or (가)~(마) labels, a specific verb + resource that an agent can immediately understand. It does not name or distinguish itself from siblings such as suggest_regions or list_regions, which is the only thing keeping it from 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 Guidelines2/5

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

The only guidance is the ordering default (random when unspecified). There is no statement of when to call this versus suggest_regions, prepare_values, or make_figures, and no prerequisites or exclusions are given.

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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