Single tool 'execute_skill_action' has a reasonable name starting with verb, but critical gaps undermine quality. Description is 97 chars (within 10-1024 range but minimal context). Input schema present with two parameters, both typed (string, integer) and described. However, no output schema is documented, LLMs cannot plan downstream calls or extract results. Parameter descriptions lack constraints (e.g., valid scenario_type values, synthetic_miles_count range). No error handling guidance. Tool definition is visible in mcp_server.py but implementation is a stub that only prints JSON; actual logic is in client.py (not integrated into MCP server). This is a proof-of-concept, not a production-ready MCP server.

• Document the complete output schema with all fields (sim_batch_id, scenario_name, synthetic_miles_executed, sensor_modalities_rendered, collision_rate_per_million_miles, adas_iso_26262_asil_d_compliance_verified, edge_case_corner_scenarios_discovered) and their types.
• Add enum constraint to 'scenario_type' parameter listing all valid scenario names (High_Speed_Highway_CutIn_Rain, Urban_Unprotected_Left_Turn_Night_Fog, etc.).
• Add min/max constraints to 'synthetic_miles_count' (e.g., 1 - 10000000) and document expected runtime per mile count.
• Expand tool description to 150 - 250 chars explaining: what the tool does (validates ADAS physics in simulation), when to use it (before deploying autonomous vehicle logic), and what it returns (compliance metrics and edge cases discovered).
• Integrate client.py logic into mcp_server.py so the tool is actually callable via MCP protocol. Currently, the server is non-functional.