Critical gaps across all dimensions. Tool definitions exist but lack rigor. Schemas are present but minimal (no parameter descriptions, no output schemas documented). Descriptions are present but generic and under-optimized for LLM selection. No error handling guidance, no parameter validation rules, no composition support. Tools appear inferred from setup.py rather than explicitly registered with full MCP metadata. This server would not pass code review.

• Document output schema for each tool. E.g., list_available_masters should return {masters: [{name: string, description: string, active: boolean}], total: number}. Include all fields the LLM needs for downstream calls.
• Expand tool descriptions to 100-200 characters. For activate_master: 'Activates a master context tool, loading its contextual sub-tools into memory. Use this to switch between different task domains (e.g., email, calendar, coding). Returns the activated master name and list of newly available tools. Fails if master does not exist.'
• Add parameter descriptions. For master_name: 'The name of the master context to activate (string, case-sensitive). Call list_available_masters() first to see valid options.'
• Add error handling guidance. E.g., 'If master not found, returns error with list of available masters. If already active, returns success with no-op. If permission denied, returns error with required scope.'
• Explicitly register all tools in server_mcp.py with full MCP tool definitions (name, description, inputSchema with typed parameters and descriptions, outputSchema). Do not infer from setup.py.