geo-figure-mcp

prepare_values

Transforms numeric datasets for exam questions into max-100 indices, sum shares, base deviations, or reference percentages, and rounds results to the required digits.

Instructions

출제용 가공: 최댓값=100 지수, 합에 대한 비율, 기준값과의 차이(편차), 기준 대비 %, 반올림.

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": {
      "values": "<values>"
    },
    "name": "prepare_values"
  }
}

Input Schema

Name	Required	Description	Default
`base`	No	deviation·national 의 기준값
`mode`	No		none
`digits`	No
`values`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Nov 9, 2026

TDQS

C2.5/5.0

Behavior2/5

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

With no annotations, the description carries the full behavioral burden, yet it does not say whether the output is a new array or an in-place change, what happens if base is missing for deviation/national, or how division by zero is handled. It does disclose that rounding occurs, which is a genuine behavioral detail, but that is the only one.

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

Conciseness3/5

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

It is a single compact clause with no filler, which is good, but the compression goes too far: the fragmentary phrasing and lack of a subject/verb make it harder to parse than a slightly longer sentence would be. Front-loading of the transformation list is effective.

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 4-parameter transformation tool with no annotations, no output schema, and 25% schema coverage, the description should explain return shape, error conditions, and the role of base and digits. It covers only the mode semantics, leaving substantial gaps an agent would need to resolve by trial.

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 description coverage is only 25%, so the description must compensate, and it partially does: each mode enum value is glossed (max100 = index to 100, share = proportion of sum, deviation = difference from base, national = % of base). Gaps remain for digits (implied by 반올림 but not stated as decimal places) and for the values array itself, which is never mentioned.

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

Purpose3/5

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

The fragment '출제용 가공' plus the list of operations (max=100 index, share of sum, deviation, % of baseline, rounding) does convey the family of numeric transformations performed. However, there is no explicit verb+object stating that it transforms an input array of numbers, and nothing distinguishes it from siblings like make_figures or assign_labels. An agent can guess the purpose but must infer it from the mode list.

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?

There is no statement of when to use this tool, when not to, or which sibling to prefer for related tasks. The word '출제용' hints at the exam-question workflow, but no alternatives or preconditions are named.

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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