A Spring Boot 4.0 application demonstrating AI engineering with Java, including LLM integration (OpenAI, Ollama, Anthropic), RAG, vector stores, and MCP (Model Context Protocol) server/client capabilities.
This server has severe definition quality issues. All 12 tools lack proper English descriptions (descriptions are in Turkish, which violates LLM-optimization principles). Tool names use underscores but lack action verbs in most cases (e.g., 'sehir_hava_ozet' instead of 'get_weather_summary'). Input schemas are present but minimal, most parameters lack detailed descriptions explaining format, constraints, or valid ranges. No output schemas are documented. Error handling is absent from visible code. The server appears to be a teaching/demo project rather than production-grade tooling.
Bugunun tarih ve saat bilgisini ver
Urun koduna gore fiyat bilgisini dondur
İki tarih arasındaki gun farkini hesaplar
Verilen tutara KDV eklenmis sonucu hesaplar
Verilen teknik kelimeyi kisaca aciklar
Metni 1 cumlelik ozetler
Konuya gore populer kutuphane listesi dondurur
All tool descriptions are in Turkish, not English. LLMs are optimized for English descriptions. Non-English descriptions reduce LLM comprehension and tool selection accuracy.
Tool names lack action verbs. Names like 'sehir_hava_ozet', 'kisa_ozet', 'kelime_anlami' do not start with verbs (get_, list_, search_, create_). LLMs cannot infer intent from noun-only names.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 29 | 2026-07-28+ | v2 |
Sehir icin nufus ve bolge bilgisini dondurur
Sehir icin kisa hava ozeti dondurur
Sehir icin hava riski ve tavsiye dondurur
Sistem saglik durumunu kontrol eder
Urun adina veya kategoriye gore arama yapar
Parameter descriptions are minimal or absent. Most parameters have only a short Turkish phrase (e.g., 'Sehir adi'). No format constraints, ranges, or valid values documented. LLMs cannot validate input without explicit constraints.
No output schemas documented. Tools return data but LLMs have no specification of response structure, field types, or required fields. This forces LLMs to guess at response format.
No error handling or recovery guidance visible. Tools provide no error messages, validation feedback, or actionable recovery steps for LLMs when calls fail.