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MCP server for conducting autonomous voice-based user interview analysis and extracting actionable product insights from interview data
Critical failures across all dimensions. The single tool 'execute_skill_action' has a minimal description (56 chars, below the 10-1024 baseline), lacks output schema documentation entirely, and the tool definition itself is not explicitly registered in the MCP server code, only inferred from client.py. The mcp_server.py file contains no actual MCP protocol implementation, just a JSON print statement. No error handling, no parameter validation guidance, no security considerations. The server does not implement the MCP specification at all.
Execute autonomous voice user interview analysis skill action
Tool definition not explicitly registered in MCP server code. Tool exists only in client.py; mcp_server.py contains no tool registration, schema binding, or MCP protocol handler.
No output schema documented. The tool returns a complex object with 6 fields (study_id, cohort, interviews_conducted_by_voice_agent, top_unmet_user_needs, sentiment_nps_correlation_score, actionable_product_features_prioritized, executive_video_clip_reel_compiled) but no schema is visible in the MCP server definition.
Tool description is too short (56 chars) and lacks context. Does not explain WHEN to use this tool, what prerequisites exist, or how results should be interpreted. LLMs cannot determine selection criteria.
No error handling or recovery guidance. Tool has no documented error cases, retryability, or actionable error messages. If the skill fails, the LLM has no guidance on next steps.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 24 | 2026-07-28+ | v2 |
Parameter descriptions lack format/constraint guidance. 'study_cohort_id' description lists example values ('STUDY_B2B_CHURN_REDUCTION', 'STUDY_ENTERPRISE_API_ADOPTION') instead of declaring an enum or pattern. LLMs may hallucinate invalid cohort IDs.