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datagokr-dev

datagokr

by datagokr-dev

fetch

Read-only

Retrieve data rows from a specified dataset using your local API key, with options for version and row count.

Instructions

본인 로컬 키로 odcloud 첫 n행을 조회합니다. Fetch rows locally with DATAGOKR_API_KEY. version 생략 시 최신 / latest version by default. 반환 / Returns: API data or request_templates; status_code=401 means access must be requested. STD_FILE은 get/download. 활용신청·파일 저장은 하지 않습니다. Does not apply for access or save files.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nNo
versionNo
dataset_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, and the description adds valuable context: it returns API data or request_templates, status_code=401 signals access must be requested, and it doesn't save files or apply for access. This goes beyond the annotation and clarifies error semantics.

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 description is front-loaded with the core purpose, followed by defaults, returns, and special cases. It is slightly redundant due to bilingual repetition, but each sentence adds value and the structure is logical.

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

Completeness4/5

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

Given the tool has an output schema and low parameter complexity, the description covers the essential behaviors: fetch semantics, default version, return types, error code, and exclusions. It is adequate for an agent to call the tool correctly.

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 0%, so the description must compensate. It explains version defaults to latest and implies n is the number of rows via '첫 n행', but it doesn't explicitly describe dataset_id or provide detailed syntax for any parameter. It adds some meaning but not complete compensation.

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 verb 'fetch' and resource 'odcloud first n rows', and specifies it queries locally with an API key. It hints at differentiation by noting that STD_FILE should use get/download, which distinguishes it from those siblings, though it doesn't explicitly name all alternatives.

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 provides partial guidance: it says it does not apply for access or save files, and that STD_FILE should use get/download. However, it doesn't explicitly state when to use this tool versus search, show, or preview, leaving some ambiguity for an agent.

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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