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MyKIFRS

by kr.eocpa

질의회신 전문

get_qna
Read-onlyIdempotent

MyKIFRS MCP의 get_qna 도구는 질의회신 전문을 반환합니다.

[Purpose]

  • The 회신 full text for a doc_number from search_qnas results or get_paragraph's related_qnas.

[Usage]

  1. "2017-001 회신 전문" → doc_number="2017-001"

  2. "GKQA01-085 원문" → doc_number="GKQA01-085"

[Response]

  • { results:[{source, doc_number, date, title, rel_stds, full_content, org}] }

  • Miss returns { error: "문서번호 없음: {doc_number}", similar:[{source, doc_number, title}] } — up to 5 부분일치 candidates.

  • source / org / rel_stds carry the same meaning as in search_qnas.

[Rules]

  • results is an array and doc_number is NOT unique — the same 번호 can appear 2+ times (measured: "GKQA03-100" → 2 rows). Never answer from the first row alone.

  • 번호 formats differ by series: "2021-G-KQA005" · "GKQA01-085" · "SSI-35551" · "2017-001" · "금감원사례-1". Pass the value as received — never alter case or separators.

  • On a miss, re-ask using similar rather than guessing another 번호.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
doc_numberYes질의회신 문서번호

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

The description goes well beyond the readOnly/idempotent annotations by disclosing that results is an array, doc_number is not unique, miss responses include a similar list with up to 5 candidates, and number formats must be preserved exactly. This gives an agent essential runtime expectations not inferable from the schema.

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 well-structured with Purpose, Usage, Response, and Rules sections, making important behavioral notes easy to find. The opening Korean sentence is somewhat redundant with the [Purpose] section, but the overall organization and front-loaded purpose keep it efficient.

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

Completeness5/5

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

For a one-parameter tool with no output schema, the description fully covers input semantics, response shape, error behavior, duplicate-row risk, and number-format variability. Nothing needed to invoke the tool correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Even though schema coverage for doc_number is 100%, the description adds substantial meaning with usage examples and critical formatting rules such as passing the value exactly as received and never altering case or separators. This is more than a restatement of the schema.

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 that get_qna returns the full text of a 질의회신 for a given doc_number, sourced from search_qnas results or get_paragraph's related_qnas. This distinguishes it from sibling search/paragraph tools by naming the resource and the source context.

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?

The [Usage] section provides concrete natural-language-to-parameter examples and says the doc_number comes from search_qnas or get_paragraph. It gives clear context for when the tool is appropriate, though it does not explicitly name alternatives or exclusion conditions.

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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TDQS

A4.8/5.0
Disambiguation5/5

The verb prefixes (get/search/list) plus distinct nouns (paragraph, qna, standards, usage_stats) make each tool's purpose unmistakable. search_standards and search_qnas target different corpora, and get_paragraph/get_qna retrieve by unique key versus full-text search, so no two tools overlap.

Naming Consistency5/5

Every tool follows the verb_noun snake_case pattern: get/retrieves by key, search/does full-text queries, list/returns the catalog. The naming perfectly mirrors the tool behavior, making the set predictable and easy to navigate.

Tool Count5/5

Six tools is well-scoped for a standards/Q&A reference server: catalog (list_standards), full-text search over two corpora (search_standards, search_qnas), keyed retrieval for each (get_paragraph, get_qna), and one telemetry tool. Each tool earns its place with no redundancy.

Completeness4/5

The core retrieval workflow is complete: list catalog → search corpus → fetch full text, and search Q&A → fetch Q&A response, with get_paragraph even linking to related Q&As. Minor gaps exist such as no way to browse a standard's full paragraph structure or enumerate Q&A documents without a search query, but agents can work around these.

Resources