Server has 5 tools with complete input/output schemas and descriptions. Naming follows verb_noun convention (transcribe_, extract_, synthesize_, build_, manage_). Descriptions are present (avg ~80 chars) but lack actionable context for LLM selection. Parameters are typed and mostly described, but lack constraints (enums, ranges, patterns). Output schemas are documented via Pydantic models. Error handling is minimal, no recovery guidance or categorization. Security: no secrets in params (good), but no audit logging or permission gates visible. Composition is reasonable (each tool has one responsibility), but session management tool mixes create/get/end into one endpoint.

• Split manage_voice_session into three separate tools: create_voice_session(patient_id), get_voice_session(session_id), end_voice_session(session_id). Each tool has one clear responsibility and LLMs can invoke them without ambiguity.
• Add enum constraints to parameters: format: enum=['wav', 'mp3', 'webm', 'ogg'], voice_profile: enum=['clinical_clear', ...], action removed (split into separate tools). Document valid values in descriptions and reject invalid inputs with actionable error messages.
• Enhance tool descriptions with workflow context. E.g., transcribe_audio: 'Convert speech to text. Call this first in a voice interaction, then pass output to extract_clinical_entities to parse medical details.' Include dependency hints and when to use each tool.
• Implement error handling with recovery guidance. Return structured errors: {error_code, message, recovery_action}. E.g., 'Invalid audio format: got "flac", must be one of: wav, mp3, webm, ogg. Retry with a supported format or convert the audio file.'
• Document output field semantics. E.g., confidence: float (0.0-1.0, higher = more certain), clarification_needed: boolean (true if entity extraction is ambiguous; if true, present clarification_questions to user before proceeding).