This server has a single tool 'execute_skill_action' with virtually no definition. The tool description is generic (23 chars) and provides no actionable guidance. No input schema is visible in the source code, the mcp_server.py file only prints a JSON status message and does not register tools with schemas, parameters, or output types. The tool is inferred to exist from the supported_tools list, not explicitly defined. No parameter descriptions, no output schema, no error handling guidance. The implementation is a stub that does not actually implement MCP protocol semantics.

• Define a complete JSON Schema for execute_skill_action with required parameters (active_streams_count: integer 1-1024, gpu_memory_pool_gb: number 1-1000) and descriptions for each.
• Expand tool description to 100-200 chars: 'Schedule continuous dynamic batching for VLM inference. Accepts active stream count and GPU memory pool size. Returns scheduler epoch ID, memory allocation, overlap efficiency %, bubble reduction %, P99 latency, and deadlock guarantee status. Use this to optimize batch scheduling for concurrent LLM requests.'
• Rename tool to 'schedule_dynamic_batching' or 'get_batch_schedule' to clarify the action and resource.
• Document the output schema with all fields: scheduler_epoch_id (string), active_streams (integer), gpu_memory_allocated_gb (number), prefill_decode_overlap_efficiency_pct (number), bubble_time_reduction_ratio_pct (number), inter_token_latency_p99_ms (integer), zero_deadlock_guarantee (boolean).
• Add error handling: return structured errors with actionable messages (e.g., 'GPU memory pool too small: requested 160GB but max available is 80GB. Reduce active_streams_count or increase gpu_memory_pool_gb.').