MCP server for searching GPTers AI Study Community knowledge with JWT authentication
Single tool 'search_gpters' has a complete input schema with 5 parameters, all typed and described. However, the tool description is in Korean and lacks LLM-optimized English guidance on when/why to use it. Output is returned as unstructured plain text (formatted string) rather than structured JSON, forcing LLMs to parse results manually. No error handling guidance, errors return generic messages like 'search 중 오류가 발생했습니다' without recovery hints. Parameter descriptions are minimal (e.g., 'top_k': '반환할 결과 수 (기본값: 5)') and lack format/range constraints. No output schema documented. Tool follows verb_noun naming (search_gpters) correctly but lacks English descriptions critical for LLM selection.
GPTers AI 스터디 커뮤니티의 지식을 검색합니다.
Output is unstructured plain text instead of structured JSON. LLMs must parse formatted strings to extract title, author, score, URL, etc. This wastes tokens and invites parsing errors.
Tool description is in Korean only ('GPTers AI 스터디 커뮤니티의 지식을 검색합니다'). English-speaking LLMs cannot understand when/why to invoke this tool. Descriptions must be in English and explain WHAT, WHEN, and any prerequisites.
Error messages are generic and non-actionable ('검색 중 오류가 발생했습니다: 서버 응답 코드 {status}'). LLMs receive no guidance on whether to retry, ask the user, or abandon the task. Missing recovery hints.
No output schema documented. LLMs do not know what fields to expect in results (title, text, owner_name, space_name, tags, created_at, url, score, original_score, recency_weight). Undocumented output forces manual parsing.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 44 | <=2025-11-25 | v2 |
Parameter 'created_within_days' lacks format/range constraints. Description says '작성일 기준 일수 (선택 사항)' but does not specify valid range (e.g., 1 - 365). LLMs may pass invalid values like 0 or 999999.