Critical failures across all dimensions. The single tool 'dynamic_registry_tools' is a catch-all wrapper that dynamically loads 200 tools from registry.json, but the actual tool definitions are not visible in source code, only inferred. However, the visible schema is severely deficient: the input schema has only two generic parameters ('payload' as string, 'paymentSignature' as string) with no per-tool differentiation, no type safety, and no validation. The description is vague marketing copy ('200 deterministic micro-tools...') rather than actionable LLM guidance. No output schema is documented. The tool violates single-responsibility principle by bundling 200 unrelated operations behind one name. Error handling is theatrical ('Ghost Mirror Trigger') rather than actionable, it returns fake success on injection attempts instead of rejecting them. No per-tool descriptions, no parameter constraints, no pagination, no chaining IDs. This is a proof-of-concept, not production-grade tooling.

• Explode the single dynamic_registry_tools into 200 individually registered tools, each with its own name (verb_noun format), description (50-200 chars), input schema with typed parameters and enums, and documented output schema.
• For each tool, add a description answering: What does it do? When should the LLM call it? What does it return? Example: 'get_weather_forecast: Fetch 5-day weather forecast for a location. Returns: {location, forecast_days: [{date, high_temp, low_temp, condition}]}.'
• Replace generic 'payload' parameter with tool-specific parameters. E.g., weather tool should have 'location' (string), 'days' (integer, 1-10), 'units' (enum: celsius|fahrenheit), not a free-form payload string.
• Document the output schema for each tool. Include all fields the LLM might need for chaining (IDs, references, timestamps). Omit internal metadata, audit fields, and raw API responses.
• Replace 'Ghost Mirror' deception with clear, actionable error messages. E.g., 'Invalid input: payload contains disallowed characters. Allowed: alphanumeric, spaces, hyphens. Retry with sanitized input.' This guides LLM self-correction.