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pokemon-mcp

pokemon-mcp

by suirenn0627
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

type_effectiveness

Calculates the type effectiveness multiplier for a given attacking type against one or two defending types in Pokemon battles.

Instructions

攻撃タイプ x 防御タイプ(複合可)の相性倍率。例: ice vs [ground, dragon] = 4.0

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": {
      "attacking_type": "<attacking_type>",
      "defending_types": "<defending_types>"
    },
    "name": "type_effectiveness"
  }
}

Input Schema

NameRequiredDescriptionDefault
attacking_typeYes
defending_typesYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

No annotations exist, so the description must disclose all behavioral traits. It states the basic calculation and handles dual types (as shown in example) but does not mention immunity, ability interactions, or return format. Adequate but not comprehensive.

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?

The description is a single sentence plus an example, with no unnecessary words. It is front-loaded and efficient, earning its place.

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?

With no output schema or annotations, the description is insufficient. It does not specify the return value type or structure, nor address edge cases like immunities or invalid types. The Japanese language may also limit broader understanding.

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 0%, so the description must compensate. The example clarifies that defending_types is an array. However, it does not explain allowed values, case sensitivity, or type name formats, leaving ambiguity.

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?

The description clearly states it calculates attack type vs defense type effectiveness multiplier, with an explicit example ('ice vs [ground, dragon] = 4.0'). It distinguishes from sibling tools like calc_accuracy and calc_damage by specifying the unique function of type matchup calculation.

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?

The description implies usage for looking up type effectiveness but does not provide explicit guidance on when to use this tool vs alternatives (e.g., calc_damage). No exclusions or when-not-to-use conditions 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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