This server has a single tool 'execute_skill_action' with virtually no definition. The tool description is present but generic (48 chars, below the 50-200 char LLM-optimized range). 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, types, or parameter descriptions. The client.py and example_usage.py files show a mock implementation that returns hardcoded responses, not a real MCP tool definition. No error handling, no parameter validation, no output schema documentation. This is a stub implementation, not a production-ready MCP server.

• Add explicit JSON Schema to tool registration with parameter types, descriptions, and constraints. Example: {"type": "object", "properties": {"task_input": {"type": "string", "description": "The task to execute..."}, "agent_engine": {"type": "string", "enum": ["AUTONOMOUS_WORKER", "..."]}}, "required": ["task_input"]}
• Expand tool description to 80-150 characters explaining: (1) What the tool does, (2) When to use it (e.g., 'Use this to execute multi-stage security scans on Kubernetes clusters'), (3) What it returns (task ID, status, artifacts). Current description is too generic.
• Rename tool to be more specific and action-oriented. Instead of 'execute_skill_action', use domain-specific names like 'run_vulnerability_scan' or 'execute_kubernetes_audit' so LLMs understand the actual capability.
• Document the output schema with all returned fields (task_protocol_id, task_status, steps_executed_count, artifacts_generated_count, openapi_compliance_score_pct, sse_event_stream_url, task_duration_seconds) with types and descriptions so agents can chain results.
• Add error handling with recovery guidance. For WRITE operations, implement a dry-run parameter or confirmation step. Return structured errors like {"error": "Invalid agent_engine", "valid_options": ["AUTONOMOUS_WORKER", "..."]} instead of raw exceptions.