lazymac-mcp

k_public_data

Access Korean government data in English, including weather, foreign exchange rates, holidays, and transport information through the lazymac-mcp server.

Instructions

Korean government data in English — weather, FX, holidays, transport

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": "k_public_data"
  }
}

Input Schema

Name	Required	Description	Default
`params`	No	Free-form params object — passed as query string for GET, JSON body for POST

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Apr 15, 2026

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states what data is available, without mentioning how it's accessed (e.g., API calls, rate limits, authentication needs), what format the output is in, or any constraints like data freshness or availability. This is inadequate for a tool with a complex parameter structure.

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 extremely concise—a single phrase listing domains—with zero wasted words. It's front-loaded and efficiently communicates the core purpose, though this brevity contributes to gaps in other dimensions.

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?

Given the complexity implied by multiple data domains, no annotations, no output schema, and a nested input schema with undefined parameters, the description is incomplete. It doesn't help the agent understand how to invoke the tool effectively or what to expect in return, making it inadequate for reliable use.

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?

The schema description coverage is 100%, but the schema only describes a generic 'params' object without specifics. The description adds no parameter semantics—it doesn't explain what parameters are needed for different data types (e.g., location for weather, date for holidays). This leaves the agent guessing about how to structure queries.

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 clearly states the tool provides Korean government data in English across multiple domains (weather, FX, holidays, transport), which is specific about the resource and scope. However, it doesn't explicitly differentiate from sibling tools like k_exchange_rate or k_market_intel, which appear related to Korean data or markets.

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 description provides no guidance on when to use this tool versus alternatives. It lists domains but doesn't specify use cases, prerequisites, or exclusions. For example, it doesn't clarify if this should be used over k_exchange_rate for FX data or how it differs from general data tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.