한국 뉴스 + 글로벌 AI/테크 뉴스를 AI 에이전트에서 바로 사용할 수 있는 MCP 서버. API 키 없이 바로 작동합니다.
Korean News Hub demonstrates moderate quality with visible tool definitions, parameter schemas, and descriptions. However, several critical gaps reduce production readiness. All 6 tools are properly registered with names, descriptions, and JSON Schema inputs. Description quality is mixed: tool descriptions are adequate (50-150 chars), but parameter descriptions lack detail and dependency hints. Schema completeness is present but minimal, parameters have type declarations but lack constraints (enums, ranges, patterns). Error handling is basic (try-except blocks present but LLM-facing recovery guidance is absent). Output schemas are undocumented, LLMs cannot plan downstream calls. Security is reasonable for a READ_ONLY news aggregator (no state mutation, no secrets), but the server lacks audit logging and rate limiting. The tool composition is sound (each tool has one responsibility), but inter-tool chains are not optimized (e.g., read_article could be called via results from search tools, but response IDs are not pre-mapped).
Generate a comprehensive daily news briefing. Combines Korean headlines + AI/tech news + Claude/Anthropic news.
Get Korean news by category.
Search news by keyword.
Read and extract article content from a URL.
Get global AI/tech news by topic.
Get current trending/headline news.
No output schemas documented. LLMs cannot predict response structure, preventing chaining and forcing exploratory calls. Tools return string (formatted markdown) but field mappings, object nesting, and pagination are invisible.
Enums and constraints documented in parameter descriptions (text) rather than formal JSON Schema constraints. LLMs parse descriptions ad-hoc and may hallucinate invalid values (e.g., 'Robotics' for tech_news topic, 'breaking' for korean_news category).
Example values in parameter descriptions ('Samsung AI', 'Claude Code', 'MCP server' in news_search). LLMs tend to reuse example values literally rather than adapting to context, causing false positives in searches.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 11 | 2026-07-28+ | v2 |
| 2026-03-09 | D | 56 | - | v1 |
No pagination support despite 'max 20' result limit. If users want more than 20 articles, there is no offset/limit/cursor mechanism to fetch additional pages. Large result sets will truncate silently.
Numeric parameters lack explicit min/max constraints in schema. 'count' defaults to 10, max 20, but schema does not declare this, LLMs may attempt count=100 or count=0, causing API misuse or empty results.
Error handling is internal (try-except blocks with logging) but responses provide minimal LLM-facing guidance. Feed parse failures append '[Feed Error] <exception>' to results but do not guide recovery ('Try searching instead', 'Check network'). read_article timeout error is human-friendly but not machine-actionable.
Tool names could be more verb-first. 'trending' (ambiguous: get trending? post trending?) vs 'get_trending_news'. 'daily_briefing' vs 'generate_daily_briefing'. Weak verbs reduce clarity for LLM tool selection.