기사 전문 조회
get_article기사 전문과 문장별 판정을 줍니다. 각 판정의 fact_status.is_current가 false면 그 문장은 이후 바뀐 사실이므로 현재 사실로 인용하지 마세요.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | 기사 slug. search_articles 결과의 slug |
get_article기사 전문과 문장별 판정을 줍니다. 각 판정의 fact_status.is_current가 false면 그 문장은 이후 바뀐 사실이므로 현재 사실로 인용하지 마세요.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | 기사 slug. search_articles 결과의 slug |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the description's added value is the semantic caveat that a sentence whose fact_status.is_current is false represents a superseded fact and must not be cited as current. That is real behavioral context beyond the structured fields, though it does not cover pagination or output shape.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no filler: the first states what is returned and the second states the critical interpretation rule. Purpose is front-loaded and the caveat follows immediately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description carries the burden of describing the return value, and it does so at a useful level (full text plus per-sentence verdicts, with the is_current flag explained). The internal structure of a verdict is not detailed, but the agent gets enough to call and interpret the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single slug parameter, so the schema already documents it fully. The description adds no syntax, format, or sourcing detail about slug beyond what the schema provides, making the baseline 3 appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a concrete verb and resource: it returns the article's full text plus per-sentence verdicts (판정). This clearly separates it from search_articles, which only finds articles, but the description never names a sibling explicitly, so differentiation is inferred rather than stated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description warns how to interpret the returned verdicts, but gives no guidance on when to call this tool versus search_articles, get_fact, or get_entity. The workflow (find a slug via search, then fetch the full text) is only implied by the schema's mention of 'search_articles 결과의 slug', not by the description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.