17 tools with mixed quality. Strengths: all tools have descriptions (10-200 chars, mostly adequate); input schemas present with type definitions and enums where appropriate (e.g., boss_search, boss_wizard). Weaknesses: descriptions are often terse and lack WHEN/WHY context; many parameters lack descriptions (e.g., boss_wizard's 'inputs' object has no description of expected structure); no documented output schemas; error handling guidance absent; no tool annotations (readOnlyHint/destructiveHint/idempotentHint); Chinese descriptions reduce LLM clarity. Tool naming is consistent (boss_* prefix) but generic, 'boss_wizard' and 'boss_chat' don't clearly signal action intent. Composition is reasonable (tools chain via security_id/job_id), but parameter descriptions often omit format/constraint details.

• Add output schemas for all tools. Document the structure of responses (e.g., boss_search returns {results: [{security_id, job_id, title, salary, ...}], total_count, next_page}). This enables LLMs to chain tools correctly.
• Expand parameter descriptions to include format, constraints, and examples. E.g., 'security_id (string, stable identifier from search/chat results, used across requests)' instead of bare 'security_id'.
• Add tool annotations: mark boss_status, boss_doctor, boss_search, boss_detail, boss_chat, boss_me, boss_cities, boss_interviews, boss_history, boss_chatmsg, boss_chat_summary as readOnlyHint=true; mark boss_greet, boss_mark, boss_exchange, boss_apply, boss_wizard as destructiveHint=true; mark boss_apply as idempotentHint=true.
• Translate all descriptions to English or provide English alongside Chinese. LLMs trained primarily on English will misinterpret or ignore Chinese descriptions.
• Document error scenarios and recovery paths. E.g., 'If boss_search returns 0 results, try broader keywords or fewer filters. If boss_greet fails with "already greeted", call boss_chat to view existing conversation.'