MCP server enabling AI expert debates - Dynamic expert generation for personalized multi-round philosophical discussions on any topic
Guru-PK MCP has 10 tools with mixed definition quality. Strengths: all tools have names that start with action verbs (start_, get_, generate_, record_, analyze_, export_); all have descriptions; input schemas are visible and use proper JSON Schema. Critical weaknesses: descriptions are consistently too brief (most under 50 chars, many under 20 chars), failing the rubric baseline of 10-1024 chars with 50-200 chars ideal for LLM optimization; parameter descriptions are minimal or missing entirely (most parameters lack descriptions); no output schemas are documented; error handling is absent from all tool definitions; no tool annotations (readOnlyHint, destructiveHint, idempotentHint); composition issues exist (start_pk_session has overlapping responsibility with generate_dynamic_experts, both related to expert management but split into two tools without clear sequencing). The server performs domain-specific expert debate orchestration but lacks production-grade tool engineering patterns.
获取问题分析的原则性指导(MCP Host端LLM使用)
导出增强的会话分析报告
动态生成专家推荐(直接生成3位专家)
获取当前专家的角色提示
获取当前会话状态
获取专家推荐的原则性指导(MCP Host端LLM使用)
获取系统帮助和介绍
记录批处理模式的多专家回答
Output schemas completely absent for all 10 tools. LLMs cannot infer response structure, downstream tool parameters, or plan multi-step workflows without documented output schemas.
Descriptions predominantly under 50 characters and mostly in Chinese without English fallback. Rubric baseline for LLM-optimized descriptions is 50-200 chars; most tools here are 19-40 chars.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-21 | D | 50 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 29 | - | v1 |
记录当前轮次的回答(序列模式)
启动专家PK会话(默认高效模式,建议先调用 generate_dynamic_experts 生成专家)
Parameter descriptions are missing or trivial. Most tools have parameters with no descriptions (e.g., analyze_question_profile.question, generate_dynamic_experts.question). Rubric requires all params to have descriptions explaining what they control and valid formats.
No tool annotations (readOnlyHint, destructiveHint, idempotentHint) present. Two WRITE tools (record_round_response, record_batch_responses) modify session state but lack destructiveHint to signal to LLMs that these are non-idempotent writes. READ tools lack readOnlyHint annotation.
No error handling documented. Tools lack guidance on how to recover from errors (e.g., invalid session_id in export_enhanced_session, mismatched expert names in record_batch_responses). Rubric requires error responses to tell LLM what to do next.
Composition ambiguity: start_pk_session and generate_dynamic_experts both manage expert generation. Relationship is unclear, should generate_dynamic_experts be called first? Are they mutually exclusive? No guidance in descriptions.
Stateful behavior hinted but not documented. Tools like get_persona_prompt, get_session_status, record_round_response reference 'current session' or 'round' state, but no explanation of session lifecycle, how session IDs work, or how context persists across calls. Protocol is stateless per 2026-07-28 spec; server-side session state must be explicit.
No pagination or result limits documented. export_enhanced_session returns 'enhanced analysis report' but schema does not specify format, length, or if pagination applies. Tools returning lists or structured data should declare limits (rubric: cap at 20-50 items, include pagination).