faceView MCP server has 9 tools with basic schemas and descriptions, but significant gaps prevent higher scores. All tools have input schemas with type definitions and descriptions present, meeting minimum requirements. However, descriptions are terse (avg ~50 chars, well below the 194-char baseline), parameter descriptions are minimal or absent, output schemas are undocumented, and error handling guidance is missing. No tool annotations (readOnlyHint/destructiveHint) despite clear read/write distinctions. Tool naming is reasonable (verb_noun pattern: send_chat, set_emotion, list_personas) but lacks parameter-level clarity. No pagination support for list_events despite accepting an 'n' parameter. The server reads like an early-stage prototype rather than production-grade tooling.

• Expand tool descriptions to 100 - 200 chars. Example for send_chat: 'Send a message to the faceView chat panel. Use this to drive conversation with the avatar or relay user input. Returns confirmation of message receipt.' Current: 52 chars.
• Document output schemas for all tools. Example for camera_state: 'Returns {presence: bool, identity: string, emotion: string, mouth_state: string}'. Example for list_events: 'Returns {events: [{type: string, timestamp: float, data: object}], total: int}'.
• Convert set_emotion 'name' parameter to enum: ['neutral', 'happy', 'sad', 'angry', 'surprised', 'fear', 'disgust', 'contempt', 'pout', 'kiss', 'pain', 'thinking']. Same for set_persona with valid persona names.
• Add tool annotations: mark camera_state, list_events, list_personas, screenshot with readOnlyHint=true. Mark send_chat, speak, set_emotion, set_persona, avatar_say with destructiveHint=true (they modify avatar state).
• Add parameter descriptions. Example for list_events 'n' param: 'Number of recent events to return (1 - 500, default 50). Larger values may impact performance.' Example for screenshot 'encode_b64': 'If true, return image as base64-encoded JPEG string; if false, save to disk and return file path.'