Seoul OpenData MCP

Call a catalog dataset (raw fields)

call_dataset

Fetch raw, non-normalized rows from a Seoul OpenData catalog dataset by ID, with optional limit and path parameters for tailored data retrieval.

Instructions

Invoke a catalog dataset by id and get RAW (non-normalized) rows; check get_dataset_spec first for required params. 카탈로그 데이터셋을 직접 호출합니다. 응답은 정규화되지 않은 원본 필드입니다.

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": {
      "id": "<id>"
    },
    "name": "call_dataset"
  }
}

Input Schema

Name	Required	Description
`id`	Yes	카탈로그 데이터셋 id
`limit`	No	최대 행 수 (기본 10, 최대 50)
`params`	No	경로 파라미터 (get_dataset_spec의 pathParams 순서대로)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Aug 17, 2026

TDQS

A3.8/5.0

Behavior3/5

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

With no annotations, the description carries the burden of behavioral disclosure. It adds the key detail that output is raw/non-normalized and references get_dataset_spec for parameters, but it omits read-only behavior, error handling, or pagination details.

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?

The English sentence is concise and front-loaded with the main action. The Korean sentence is a direct translation that adds redundancy, though it is short and doesn't significantly harm clarity.

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

Completeness3/5

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

Given the absence of annotations and output schema, the description mentions raw rows and the spec-check prerequisite but does not clarify the tool's scope (generic for all catalog datasets) or return value structure. This is adequate but leaves gaps for an agent to fully understand behavior.

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 100%, so parameters are already documented with meanings (e.g., limit default/max, params order). The description adds only a dependency hint to get_dataset_spec, which is already referenced in the params schema, providing no additional semantic value.

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 uses a specific verb ('Invoke') with a clear resource ('catalog dataset by id') and states the output ('RAW non-normalized rows'). It distinguishes itself from siblings like get_dataset_spec by emphasizing data invocation rather than metadata retrieval.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

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

It explicitly instructs to 'check get_dataset_spec first for required params,' establishing a prerequisite for use. However, it does not explicitly contrast with alternatives like search_dataset or specialized getters, leaving some ambiguity about when to use this over them.

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